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16 Data Science Projects with Source Code to Strengthen your Resume

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Tried to build some data science projects to improve your resume and got intimidated by the size of the code and the number of concepts used? Does it feel too out of reach, and did it crush your dreams of becoming a data scientist? We have collected for you sixteen data science projects with source code so you can actually participate in the real-time projects of data science. These will help boost confidence and also tell the interviewer that you’re serious about data science.

Do you know?

Finding a perfect idea for your project is something that concerns you more than implementing the project itself, isn’t it? So keeping the same in mind, we have compiled a list of over 500+ project ideas just for you. All you have to do is bookmark this article and get started.

  • Python Projects
  • Python Django (Web Development) Projects
  • Python Game Development Projects
  • Python Artificial Intelligence Projects
  • Python Machine Learning Projects
  • Python Data Science Projects
  • Python Deep Learning Projects
  • Python Computer Vision Projects
  • Python Internet of Things Projects

In this blog, we will list out different data science project examples in the languages R and Python. Let’s separate these on the basis of difficulty so you have a proper path to follow.

Top Data Science Project Ideas

Here are the best data science project ideas with source code:

1. Beginner Data Science Projects

1.1 fake news detection.

Drive your career to new heights by working on Data Science Project for Beginners  –  Detecting Fake News with Python

A king of yellow journalism, fake news is false information and hoaxes spread through social media and other online media to achieve a political agenda. In this data science project idea, we will use Python to build a model that can accurately detect whether a piece of news is real or fake. We’ll build a TfidfVectorizer and use a PassiveAggressiveClassifier to classify news into “Real” and “Fake”. We’ll be using a dataset of shape 7796×4 and execute everything in Jupyter Lab.

Language:  Python

Dataset/Package:  news.csv

1.2 Road Lane Line Detection

Check the complete implementation of Lane Line Detection Data Science Project:  Real-time Lane Line Detection in Python

Data Science Project Idea:  The lines drawn on the roads guide human drivers where the lanes are. It also refers to the direction to steer the vehicle. This application is cardinal for developing driverless cars.

You can build an application having the ability to identify track lines from input images or continuous video frames.

1.3 Sentiment Analysis

Check the complete implementation of Data Science Project with Source Code –  Sentiment Analysis Project in R

Sentiment analysis is the act of analyzing words to determine sentiments and opinions that may be positive or negative in polarity. This is a type of classification where the classes may be binary (positive and negative) or multiple (happy, angry, sad, disgusted,..). We’ll implement this data science project in the language R and use the dataset by the ‘janeaustenR’ package. We will use general-purpose lexicons like AFINN, bing, and loughran, perform an inner join, and in the end, we’ll build a word cloud to display the result.

Language:  R

Dataset/Package:  janeaustenR

1.4 Detecting Parkinson’s Disease

Put your best foot forward by working on Data Science Project Idea –  Detecting Parkinson’s Disease with XGBoost

We have started using data science to improve healthcare and services – if we can predict a disease early, it has many advantages on the prognosis. So in this data science project idea, we will learn to detect Parkinson’s Disease with Python. This is a neurodegenerative, progressive disorder of the central nervous system that affects movement and causes tremors and stiffness. This affects dopamine-producing neurons in the brain and every year, it affects more than 1 million individuals in India.

Language:  Python

Dataset/Package:  UCI ML Parkinsons dataset

1.5 Color Detection with Python

Build an application to detect colors with Beginner Data Science Project –  Color Detection with OpenCV

How many times has it occurred to you that even after seeing, you don’t remember the name of the color? There can be 16 million colors based on the different RGB color values but we only remember a few. So in this project, we are going to build an interactive app that will detect the selected color from any image. To implement this we will need a labeled data of all the known colors then we will calculate which color resembles the most with the selected color value.

Dataset:  Codebrainz Color Names

1.6 Brain Tumor Detection with Data Science

Data Science Project Idea:  There are many famous deep learning projects on MRI scan dataset. One of them is Brain Tumor detection. You can use transfer learning on these MRI scans to get the required features for classification. Or you can train your own convolution neural network from scratch to detect brain tumors.

Dataset:  Brain MRI Image Dataset

1.7 Leaf Disease Detection

Data Science Project Idea:  Disease detection in plants plays a very important role in the field of agriculture. This Data Science project aims to provide an image-based automatic inspection interface. It involves the use of self designed image processing and deep learning techniques. It will categorize plant leaves as healthy or infected.

Dataset:  Leaf Dataset

2. Intermediate Data Science Projects

2.1 speech emotion recognition.

Explore the complete implementation of Data Science Project Example  –  Speech Emotion Recognition with Librosa

Let’s learn to use different libraries now. This data science project uses librosa to perform Speech Emotion Recognition. SER is the process of trying to recognize human emotion and affective states from speech. Since we use tone and pitch to express emotion through voice, SER is possible; but it is tough because emotions are subjective and annotating audio is challenging. We’ll use the mfcc, chroma, and mel features and use the RAVDESS dataset to recognize emotion on. We’ll build an MLPClassifier for the model.

Dataset/Package:  RAVDESS dataset

2.2 Gender and Age Detection with Data Science

Put the pedal to the metal & impress recruiters with ultimate Data Science Project –  Gender and Age Detection with OpenCV

This is an interesting data science project with Python. Using just one image, you’ll learn to predict the gender and age range of an individual. In this, we introduce you to Computer Vision and its principles. We’ll build a  Convolutional Neural Network   and use models trained by Tal Hassner and Gil Levi for the Adience dataset. We’ll use some  .pb, .pbtxt, .prototxt, and .caffemodel  files along the way.

Dataset/Package:  Adience

2.3 Diabetic Retinopathy

Data Science Project Idea:  Diabetic Retinopathy is a leading cause of blindness. You can develop an automatic method of diabetic retinopathy screening. You can train a neural network on retina images of affected and normal people. This project will classify whether the patient has retinopathy or not.

Dataset:  Diabetic Retinopathy Dataset

2.3 Uber Data Analysis in R

Check the complete implementation of Data Science Project with Source Code –  Uber Data Analysis Project in R

This is a data visualization project with ggplot2 where we’ll use R and its libraries and analyze various parameters like trips by the hours in a day and trips during months in a year. We’ll use the Uber Pickups in New York City dataset and create visualizations for different time-frames of the year. This tells us how time affects customer trips.

Dataset/Package:  Uber Pickups in New York City dataset

2.4  Driver Drowsiness detection in Python

Drive your career to new heights by working on Top Data Science Project  –  Drowsiness Detection System with OpenCV & Keras

Drowsy driving is extremely dangerous and around thousands of accidents happen each year due to drivers falling asleep while driving. In this Python project, we will build a system that can detect sleepy drivers and also alert them by beeping alarm.

This project is implemented using Keras and OpenCV. We will use OpenCV for face and eye detection and with Keras, we will classify the state of the eye (Open or Close) using Deep neural network techniques.

2.5 Chatbot Project in Python

Build a chatbot using Python & step up in your career –  Chatbot with NLTK & Keras

Chatbots are an essential part of the business. Many businesses has to offer services to their customers and it needs a lot of manpower, time and effort to handle customers. The chatbots can automate most of the customer interaction by answering some of the frequent questions that are asked by the customers. There are mainly two types of chatbots: Domain-specific and Open-domain chatbots. The domain-specific chatbot is often used to solve a particular problem. So you need to customize it smartly to work effectively in your domain. The Open-domain chatbots can be asked any type of question so it requires huge amounts of data to train.

Dataset:  Intents json file

2.6 Handwritten Digit Recognition Project

Practically implement the Deep Learning Project with Source Code –  Handwritten Digit Recognition with CNN

The MNIST dataset of handwritten digits is widespread among the data scientists and machine learning enthusiasts. It is an amazing project to get started with the data science and understand the processes involved in a project. The project is implemented using the Convolutional Neural Networks and then for real-time prediction we also build a nice graphical user interface to draw digits on a canvas and then the model will predict the digit.

Dataset:  MNIST

Get hired as a data scientist with  Top Data Science Interview Questions

3. Advanced Data Science Projects

3.1 image caption generator project in python.

This is an interesting data science project. Describing what’s in an image is an easy task for humans but for computers, an image is just a bunch of numbers that represent the color value of each pixel. So this is a difficult task for computers to understand what is in the image and then generating the description in Natural language like English is another difficult task. This project uses deep learning techniques where we implement a Convolutional neural network (CNN) with Recurrent Neural Network( LSTM) to build the image caption generator.

Dataset:  Flickr 8K

Framework:  Keras

3.2 Credit Card Fraud Detection Project

Put your best foot forward by working on Data Science Projects  –  Credit Card Fraud Detection with Machine Learning

By now, you’ve begun to understand the methods and concepts. Let’s move on to some advanced data science projects. In this project, we’ll use R with algorithms like  Decision Trees , Logistic Regression, Artificial Neural Networks, and Gradient Boosting Classifier. We’ll use the Card Transactions dataset to classify credit card transactions into fraudulent and genuine. We’ll fit the different models and plot performance curves for them.

Dataset/Package:  Card Transactions dataset

3.3 Movie Recommendation System

Explore the implementation of the Best Data Science Project with Source Code-  Movie Recommendation System Project in R

In this data science project, we’ll use R to perform a movie recommendation through machine learning. A recommendation system sends out suggestions to users through a filtering process based on other users’ preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A – they might like it too. This keeps customers engaged with the platform.

Dataset/Package:  MovieLens dataset

3.4 Customer Segmentation

Put the medal to the pedal & impress recruiters with Data Science Project (Source Code included) –  Customer Segmentation with Machine Learning

This is one of the most popular projects in Data Science. Before running any campaign companies create different groups of customers.

Customer Segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base. They divide customers into groups according to common characteristics like gender, age, interests, and spending habits so they can market to each group effectively. We’ll use  K-means clustering  and also visualize the gender and age distributions. Then, we’ll analyze their annual incomes and spending scores.

Dataset/Package:  Mall_Customers dataset

3.5 Breast Cancer Classification

Check the complete implementation of Data Science Project in Python –  Breast Cancer Classification with Deep Learning

Coming back to the medical contributions of data science, let’s learn to detect breast cancer with Python. We’ll use the IDC_regular dataset to detect the presence of Invasive Ductal Carcinoma, the most common form of breast cancer. It develops in a milk duct invading the fibrous or fatty breast tissue outside the duct. In this data science project idea, we’ll use  Deep Learning  and the Keras library for classification.

Dataset/Package:  IDC_regular

3.6 Traffic Signs Recognition

Achieve accuracy in self-driving cars technology with Data Science Project on  Traffic Signs Recognition using CNN  with Source Code 

Traffic signs and rules are very important that every driver must follow to avoid any accident. To follow the rule one must first understand how the traffic sign looks like. A human has to learn all the traffic signs before they are given the license to drive any vehicle. But now autonomous vehicles are rising and there will be no human drivers in the upcoming future. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. The German Traffic signs recognition benchmark dataset (GTSRB) is used to build a Deep Neural Network to recognize the class a traffic sign belongs to. We also build a simple GUI to interact with the application.

Dataset:  GTSRB (German Traffic Sign Recognition Benchmark)

The source code of all these data science projects is available on DataFlair. Get started now and build a project in Data Science. Follow from beginner to advanced, and once you’re done, you can move on to other projects.

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  • Data and data management

8 data science projects to build your resume

A strong data science resume includes a variety of projects. find out which data science project types employers are looking for and how to present them on your resume..

Linda Rosencrance

  • Linda Rosencrance

Writing a specific resume to apply for a data science position is no easy task. However, it is necessary, as applicants need to submit resumes for any open data science position. A well-written resume is the most critical component of getting an interview for a job as a data scientist.

A good data science resume should be brief -- typically, just one page long, unless the applicant has many years of experience. The sections of the data science resume should include:

  • Resume objective
  • Certifications
  • Publications

These sections help applicants demonstrate their backgrounds and knowledge in relevant areas.

Organizations looking to hire data scientists expect candidates to have either some previous work experience or, alternatively, data science-related projects. Job seekers transitioning to careers in data science right from college, switching careers or seeking different types of data science jobs can use projects to show prospective employers they have the necessary skills to do the work. A data science project portfolio should include three to five projects that showcase the applicant's relevant skills.

Here are eight data science projects to build your resume.

Sentiment analysis

Today, data-driven companies use sentiment analysis to identify customers' attitudes about their products or services. Sentiment analysis is the automated process of determining if opinions toward a product or service are positive, negative or neutral. Normally, this is expressed in pieces of text.

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The objective of sentiment analysis is to help a company figure out the answers to questions such as:

  • Why don't customers like the product or service?
  • Why isn't the product or service hitting its target sales goals?
  • How can the product or service be changed so more customers like it?
  • What factors affect customer sentiment toward the product or service, e.g., quality, quantity, price or something else?

Customer opinions can range from positive to negative, and the range of responses can be classed as positive, negative or multiple -- i.e., excited, angry, happy, sad or another emotion.

This sentiment analysis data science project could be implemented in the R language , using the "janeaustenR" package or data set. For this project, the job candidate will use general-purpose lexicons, including:

  • Bing, which labels words as positive or negative.
  • AFINN, a list of words rated for valence characterizing and categorizing specific emotions.
  • An integer between minus five and plus five.

The applicant can then build a word cloud to display the results.

Real-time face detection

Face detection , a method to distinguish a person's face from other parts of the body and the background, is a simpler undertaking and can be considered a beginner-level project.

The objective of face detection is to determine if there are any faces in an image or video. If there is more than one face in the image or video, each face is enclosed by a bounding box. A job applicant should be able to build a simple face detector using Python . Building a program that detects faces is a great way to get started with computer vision .

The module library used for this project is called the Open Source Computer Vision Library (OpenCV), an open source computer vision and machine learning library with a focus on real-time applications.

Face detection is one of the steps needed for facial recognition, the procedural recognition of a person's face along with the user's authorized name. The best method for facial recognition is to use deep neural networks.

After a face is detected, deep learning can solve face recognition tasks, using such transfer learning models as VGG16 architecture, ResNet50 architecture and FaceNet architecture. These make it easier to build deep learning models, enabling users to build high-quality face recognition systems. Users can also build their own deep learning models to build face recognition systems. Face recognition models can be used in security systems and surveillance, for example.

Spam detection

Spam detection is a classic data science problem, as organizations need to monitor their communication channels for spam emails and messages to ward off data security threats. Google, Yahoo and other major email providers implement spam detection algorithms to handle the threats posed by spam emails.

Training a model to detect spam messages and spam emails is another project for data science applicants to use to build their resumes.

Project: Spam classification Tools: Scikit-learn, Spacy, NLTK, Python Data set: SMS Spam Collection Dataset from Kaggle

Data storytelling and visualization

Using data to provide insights, tell stories and convince people of something is an important part of a data science job. What good is doing a top-notch analysis if the CEO doesn't understand it or take action based on it?

This data science project should enable laypeople, such as hiring managers with little coding or statistical backgrounds, to draw the appropriate conclusions. Data visualization and communication skills are important for this project to show and explain the applicant's code.

One example is doing a data visualization project using ggplot2 (a data visualization package for the statistical programming language R) and its libraries to analyze certain parameters, such as the number of trips a Boston Uber driver makes in one day, one month, three months, six months or 12 months. The applicant will use Uber pickups in the Boston data set, for instance, and create visualizations for the different time frames of the year. This reveals how time affects customer trips.

Project: Uber data analysis project in R Language: R Data set: Uber pickups in Boston

Recommender system

A recommender system , a platform that uses a filtering process, offers users various content based on their preferences. A recommender system inputs information about the user, evaluates those parameters using a machine learning model and returns recommendations -- for example, with movie recommendations.

A movie recommendation can be based on input received from people who have seen a particular film. Their responses can classify a movie as funny, boring, interesting, exciting or even a waste of time.

There are two types of recommender systems:

  • Content-based system. This offers recommendations based on the data a user provides. The system generates a user profile based on that data, which it then uses to make suggestions to the user. As the user inputs more data or takes certain actions based on the recommendations, the recommendation engine becomes increasingly more accurate. The recorded activity allows an algorithm to offer suggestions on movies if they're similar to those the user liked in the past.
  • Collaborative system. This offers recommendations based on information about other users with similar viewing histories or preferences. Recording users' preferences enables a collaborative system to cluster similar users and provide recommendations based on the activities of users in the same group.

Netflix, for example, recommends movies or shows that are similar to a user's browsing history or movies that other users with similar browsing histories have watched in the past.

Project: Movie recommendation system project in R Language: R Data set: MovieLens dataset

Optical character recognition

This data science project is great for beginners. Optical character recognition ( OCR ) uses an electronic or mechanical device to convert two-dimensional text data into a form of machine-encoded text. Computer vision can be used to read the text files or image. After reading the image, use the Python-pytesseract module (an OCR tool for Python) to read the text data in the PDF or image. Then convert the text data into a string of data that can be displayed in Python.

Once data science job applicants thoroughly understand how OCR works and the necessary tools, they can compute more complex problems, such as using sequence-to-sequence attention models to convert the data the OCR reads from one language into another.

Time series prediction

Time series prediction is the study of how metrics behave over time. The time series technique is commonly used in data science with a wide range of applications, including weather forecasting, predicting sales, analyzing annual trends and analyzing website traffic.

The increase in traffic to a website can be a major problem for a company, as it can cause the site to load slowly or crash entirely. Predicting the website traffic can enable the company to make better decisions to control the congestion.

Project: Web traffic time series forecasting Tools: Google Cloud Platform Algorithms: Recurrent neural networks , long- and short-term memory, autoregressive integrated moving average-based techniques Data set: The data set consists of 145,000 time series, representing the number of daily page views of different Wikipedia articles.

Data sources

One of the key decisions data science job applicants have to make is what data to analyze with any project.

Here are some websites where applicants can find data to work with.

  • Kaggle. The world's largest data science community that offers tools and resources to help users achieve their data science goals. Includes different types of data sets of varying sizes that users can download for free.
  • Data Portals . A comprehensive list of 590 (to date) open data portals from around the globe, each of which offers its own library of data sets. The data portal is curated by a group of open data experts, including representatives from local, regional and national governments and international organizations, such as the World Bank, and many nongovernmental organizations.
  • Data.gov. The home of the U.S. government's open data, which includes data, tools and resources for conducting research, developing web and mobile applications, and designing data visualizations.
  • Open Data on AWS. The Registry of Open Data on AWS makes it easy to find data sets publicly available through Amazon services.
  • Academic Torrents. A distributed system for sharing massive data sets. The site facilitates the storage of all the data used in research, including data sets and publications.

How to add data science projects to a resume

The best projects to showcase are ones that can be presented succinctly. A well-constructed description of the project can be presented in a few sentences to a paragraph.

When adding data science projects to a resume, applicants should include:

  • The name of the project.
  • A description of the role -- was this a personal effort or a team effort?
  • A brief explanation of the purpose of the project.
  • A couple sentences about how the project was built.
  • The tools that were used.
  • What the project accomplished.
  • A sentence about how the same principle could apply in business.
  • A link to the project -- a website that offers data science job applications the opportunity to showcase all their personal projects in depth.
  • A link to the code.

Although many recruiters and hiring managers will follow links and look at candidates' project presentations on their websites or portfolio sites, some will only look at a candidate's GitHub .

As such, applicants should know the basics of GitHub and be familiar with Git -- a version control system they can use to manage and keep track of their source code histories.

Data scientists are in high demand. Consequently, there's enormous potential for growth in this field for skilled professionals. To break into the field of data science, job applicants must impress prospective employers by showcasing their skills and expertise. They can demonstrate they have the necessary skills by adding data science projects to their resumes.

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16 Data Science Projects with Source Code to Strengthen your Resume

Free Machine Learning courses with 130+ real-time projects Start Now!!

Tried to build some data science projects to improve your resume and got intimidated by the size of the code and the number of concepts used? Does it feel too out of reach, and did it crush your dreams of becoming a data scientist? We have collected for you sixteen data science projects with source code so you can actually participate in the real-time projects of data science. These will help boost confidence and also tell the interviewer that you’re serious about data science.

Do you know?

Finding a perfect idea for your project is something that concerns you more than implementing the project itself, isn’t it? So keeping the same in mind, we have compiled a list of over 500+ project ideas just for you. All you have to do is bookmark this article and get started.

  • Python Projects
  • Python Django (Web Development) Projects
  • Python Game Development Projects
  • Python Artificial Intelligence Projects
  • Python Machine Learning Projects
  • Python Data Science Projects
  • Python Deep Learning Projects
  • Python Computer Vision Projects
  • Python Internet of Things Projects

In this blog, we will list out different data science project examples in the languages R and Python. Let’s separate these on the basis of difficulty so you have a proper path to follow.

Top Data Science Project Ideas

Here are the best data science project ideas with source code:

1. Beginner Data Science Projects

1.1 fake news detection.

Drive your career to new heights by working on Data Science Project for Beginners  – Detecting Fake News with Python

python project detecting fake news - data science project ideas

A king of yellow journalism, fake news is false information and hoaxes spread through social media and other online media to achieve a political agenda. In this data science project idea, we will use Python to build a model that can accurately detect whether a piece of news is real or fake. We’ll build a TfidfVectorizer and use a PassiveAggressiveClassifier to classify news into “Real” and “Fake”. We’ll be using a dataset of shape 7796×4 and execute everything in Jupyter Lab.

Language:  Python

Dataset/Package: news.csv

1.2 Road Lane Line Detection

Check the complete implementation of Lane Line Detection Data Science Project: Real-time Lane Line Detection in Python

Data Science Project Idea:  The lines drawn on the roads guide human drivers where the lanes are. It also refers to the direction to steer the vehicle. This application is cardinal for developing driverless cars.

You can build an application having the ability to identify track lines from input images or continuous video frames.

1.3 Sentiment Analysis

Check the complete implementation of Data Science Project with Source Code – Sentiment Analysis Project in R

Data-Science R Project Sentiment Analysis

Sentiment analysis is the act of analyzing words to determine sentiments and opinions that may be positive or negative in polarity. This is a type of classification where the classes may be binary (positive and negative) or multiple (happy, angry, sad, disgusted,..). We’ll implement this data science project in the language R and use the dataset by the ‘janeaustenR’ package. We will use general-purpose lexicons like AFINN, bing, and loughran, perform an inner join, and in the end, we’ll build a word cloud to display the result.

Language: R

Dataset/Package: janeaustenR

1.4 Detecting Parkinson’s Disease

Put your best foot forward by working on Data Science Project Idea – Detecting Parkinson’s Disease with XGBoost

Python machine learning project - data science project ideas

We have started using data science to improve healthcare and services – if we can predict a disease early, it has many advantages on the prognosis. So in this data science project idea, we will learn to detect Parkinson’s Disease with Python. This is a neurodegenerative, progressive disorder of the central nervous system that affects movement and causes tremors and stiffness. This affects dopamine-producing neurons in the brain and every year, it affects more than 1 million individuals in India.

Language: Python

Dataset/Package: UCI ML Parkinsons dataset

1.5 Color Detection with Python

Build an application to detect colors with Beginner Data Science Project – Color Detection with OpenCV

project in python on color detection

How many times has it occurred to you that even after seeing, you don’t remember the name of the color? There can be 16 million colors based on the different RGB color values but we only remember a few. So in this project, we are going to build an interactive app that will detect the selected color from any image. To implement this we will need a labeled data of all the known colors then we will calculate which color resembles the most with the selected color value.

Language:  Python

Dataset:  Codebrainz Color Names

1.6 Brain Tumor Detection with Data Science

Data Science Project Idea: There are many famous deep learning projects on MRI scan dataset. One of them is Brain Tumor detection. You can use transfer learning on these MRI scans to get the required features for classification. Or you can train your own convolution neural network from scratch to detect brain tumors.

Dataset: Brain MRI Image Dataset

1.7 Leaf Disease Detection

Data Science Project Idea: Disease detection in plants plays a very important role in the field of agriculture. This Data Science project aims to provide an image-based automatic inspection interface. It involves the use of self designed image processing and deep learning techniques. It will categorize plant leaves as healthy or infected.

Dataset: Leaf Dataset

2. Intermediate Data Science Projects

2.1 speech emotion recognition.

Explore the complete implementation of Data Science Project Example  – Speech Emotion Recognition with Librosa

Python project - speech emotion recognition

Let’s learn to use different libraries now. This data science project uses librosa to perform Speech Emotion Recognition. SER is the process of trying to recognize human emotion and affective states from speech. Since we use tone and pitch to express emotion through voice, SER is possible; but it is tough because emotions are subjective and annotating audio is challenging. We’ll use the mfcc, chroma, and mel features and use the RAVDESS dataset to recognize emotion on. We’ll build an MLPClassifier for the model.

Dataset/Package: RAVDESS dataset

2.2 Gender and Age Detection with Data Science

Put the pedal to the metal & impress recruiters with ultimate Data Science Project – Gender and Age Detection with OpenCV

Python project age and gender detection

This is an interesting data science project with Python. Using just one image, you’ll learn to predict the gender and age range of an individual. In this, we introduce you to Computer Vision and its principles. We’ll build a Convolutional Neural Network and use models trained by Tal Hassner and Gil Levi for the Adience dataset. We’ll use some .pb, .pbtxt, .prototxt, and .caffemodel files along the way.

Dataset/Package: Adience

2.3 Diabetic Retinopathy

Data Science Project Idea: Diabetic Retinopathy is a leading cause of blindness. You can develop an automatic method of diabetic retinopathy screening. You can train a neural network on retina images of affected and normal people. This project will classify whether the patient has retinopathy or not.

Dataset: Diabetic Retinopathy Dataset

2.3 Uber Data Analysis in R

Check the complete implementation of Data Science Project with Source Code – Uber Data Analysis Project in R

Data Science R Project Uber Data Analysis

This is a data visualization project with ggplot2 where we’ll use R and its libraries and analyze various parameters like trips by the hours in a day and trips during months in a year. We’ll use the Uber Pickups in New York City dataset and create visualizations for different time-frames of the year. This tells us how time affects customer trips.

Dataset/Package: Uber Pickups in New York City dataset

2.4  Driver Drowsiness detection in Python

Drive your career to new heights by working on Top Data Science Project  – Drowsiness Detection System with OpenCV & Keras

Data Science Project Ideas - Driver Drowsiness Detection System

Drowsy driving is extremely dangerous and around thousands of accidents happen each year due to drivers falling asleep while driving. In this Python project, we will build a system that can detect sleepy drivers and also alert them by beeping alarm.

This project is implemented using Keras and OpenCV. We will use OpenCV for face and eye detection and with Keras, we will classify the state of the eye (Open or Close) using Deep neural network techniques.

2.5 Chatbot Project in Python

Build a chatbot using Python & step up in your career – Chatbot with NLTK & Keras

Python chatbot project

Chatbots are an essential part of the business. Many businesses has to offer services to their customers and it needs a lot of manpower, time and effort to handle customers. The chatbots can automate most of the customer interaction by answering some of the frequent questions that are asked by the customers. There are mainly two types of chatbots: Domain-specific and Open-domain chatbots. The domain-specific chatbot is often used to solve a particular problem. So you need to customize it smartly to work effectively in your domain. The Open-domain chatbots can be asked any type of question so it requires huge amounts of data to train.

Dataset:  Intents json file

2.6 Handwritten Digit Recognition Project

Practically implement the Deep Learning Project with Source Code –  Handwritten Digit Recognition with CNN

python deep learning project - handwritten digit recognition

The MNIST dataset of handwritten digits is widespread among the data scientists and machine learning enthusiasts. It is an amazing project to get started with the data science and understand the processes involved in a project. The project is implemented using the Convolutional Neural Networks and then for real-time prediction we also build a nice graphical user interface to draw digits on a canvas and then the model will predict the digit.

Dataset: MNIST

Get hired as a data scientist with Top Data Science Interview Questions

3. Advanced Data Science Projects

3.1 image caption generator project in python.

Check the complete implementation of data science project with source code – Image Caption Generator with CNN & LSTM

python based project - image caption generator with CNN and LSTM

This is an interesting data science project. Describing what’s in an image is an easy task for humans but for computers, an image is just a bunch of numbers that represent the color value of each pixel. So this is a difficult task for computers to understand what is in the image and then generating the description in Natural language like English is another difficult task. This project uses deep learning techniques where we implement a Convolutional neural network (CNN) with Recurrent Neural Network( LSTM) to build the image caption generator.

Dataset: Flickr 8K

Framework: Keras

3.2 Credit Card Fraud Detection Project

Put your best foot forward by working on Data Science Projects  – Credit Card Fraud Detection with Machine Learning

Data Science R Project Credit Card Fraud Detection using ML - Data Science Project Ideas

By now, you’ve begun to understand the methods and concepts. Let’s move on to some advanced data science projects. In this project, we’ll use R with algorithms like Decision Trees , Logistic Regression, Artificial Neural Networks, and Gradient Boosting Classifier. We’ll use the Card Transactions dataset to classify credit card transactions into fraudulent and genuine. We’ll fit the different models and plot performance curves for them.

Dataset/Package: Card Transactions dataset

3.3 Movie Recommendation System

Explore the implementation of the Best Data Science Project with Source Code- Movie Recommendation System Project in R

data science movie recommendation project - data science projects

In this data science project, we’ll use R to perform a movie recommendation through machine learning. A recommendation system sends out suggestions to users through a filtering process based on other users’ preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A – they might like it too. This keeps customers engaged with the platform.

Dataset/Package: MovieLens dataset

3.4 Customer Segmentation

Put the medal to the pedal & impress recruiters with Data Science Project (Source Code included) – Customer Segmentation with Machine Learning

Data Science R project customer segmentation

This is one of the most popular projects in Data Science. Before running any campaign companies create different groups of customers.

Customer Segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base. They divide customers into groups according to common characteristics like gender, age, interests, and spending habits so they can market to each group effectively. We’ll use K-means clustering and also visualize the gender and age distributions. Then, we’ll analyze their annual incomes and spending scores.

Dataset/Package: Mall_Customers dataset

3.5 Breast Cancer Classification

Check the complete implementation of Data Science Project in Python – Breast Cancer Classification with Deep Learning

project in python breast cancer classification - data science project ideas

Coming back to the medical contributions of data science, let’s learn to detect breast cancer with Python. We’ll use the IDC_regular dataset to detect the presence of Invasive Ductal Carcinoma, the most common form of breast cancer. It develops in a milk duct invading the fibrous or fatty breast tissue outside the duct. In this data science project idea, we’ll use Deep Learning and the Keras library for classification.

Dataset/Package: IDC_regular

3.6 Traffic Signs Recognition

Achieve accuracy in self-driving cars technology with Data Science Project on  Traffic Signs Recognition using CNN  with Source Code 

python data science project on traffic signs recognition

Traffic signs and rules are very important that every driver must follow to avoid any accident. To follow the rule one must first understand how the traffic sign looks like. A human has to learn all the traffic signs before they are given the license to drive any vehicle. But now autonomous vehicles are rising and there will be no human drivers in the upcoming future. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. The German Traffic signs recognition benchmark dataset (GTSRB) is used to build a Deep Neural Network to recognize the class a traffic sign belongs to. We also build a simple GUI to interact with the application.

Dataset: GTSRB (German Traffic Sign Recognition Benchmark)

The source code of all these data science projects is available on DataFlair. Get started now and build a project in Data Science. Follow from beginner to advanced, and once you’re done, you can move on to other projects.

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data science projects to put on resume

Really wonderful article.. I am very happy to read it. Good Projects..

data science projects to put on resume

Thanks for your kind words. Share these Data Science Projects on social media with your friends & colleagues and spread the knowledge.

data science projects to put on resume

Thank you for sharing this information who has a career stating data science is very helpful for better understanding.

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yes its true

data science projects to put on resume

Probabaly one of the best article ever come across. Ocean of information in one page.

Thank you for your kind words. Share these data science projects on social media so that other aspirants can also benefit from it.

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Nice one,this is really informative n kudos to u for d enlightment. Please can i v a PDF of this? I will appreciate ur gesture if u can send it to my email. Thanks

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Thank you, great roadway for DS

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This was really informative and useful.Can you provide us with some other projects? It would be really helpful to gain even more knowledge on this ?

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Hello Ichsan,

We are happy to help you. Do give us a rating on Google and follow us on Facebook for new updates.

data science projects to put on resume

Really Informative article… New Ideas

Hey Amruta,

We are happy to help you. Do rate us on Google to give your feedback.

data science projects to put on resume

can we have more project of R language?

data science projects to put on resume

Hi, How can i use this excellent projects in my resume? The code is written by someone else and i can’t put this projects in my resume because i have not write the code.

data science projects to put on resume

yes sir, how can you put this in our resume..it will be more or less like copying hahaha thanks a lot for good projects ,god bless you

data science projects to put on resume

I can use any of these for my MSc project/dissertation?

data science projects to put on resume

Great project ideas for Data Science beginners I gained a lot of ideas for future implementation…. Thanks again Data Flair

data science projects to put on resume

I just completed reading all sections and truly it’s an amazing read. I am currently undergoing my master program in Big Data Analytics and from this blog, i realized we are doing most of what a Data Scientist do and i am glad about that. I wish the author could transform this particular blog post into a book,because it is well structured, detailed and easily to understand. Please if you have a book format of what what is on this blog i am interested so that during my spare time i can always read through and keep my memory fresh. Thumbs up for the Author, you truly did a great job on this post. Thank You for the detailed knowledge i was not taught in class.

data science projects to put on resume

sir can you provide Indian cost of living analysis project source code

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18 Data Scientist Resume Examples for 2024

Stephen Greet

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Writing Your Data Scientist Resume

We’ve reviewed countless data scientist resumes and have made a concerted effort to distill what works and what doesn’t about each of them.

Our number one tip to create an effective data science resume is to quantify your impact on the business ! These 18 data scientist resume samples below and our  data scientist cover letter templates  can help you build a great job application in 2024, no matter your career stage.

Whether you’re looking for your first job as an entry-level data scientist or are a veteran with 10+ years of expertise, you’ll find plenty of tools to build your perfect resume, like our new  Word resume examples  or  free Google Docs resume templates .

Data Scientist Resume Example

or download as PDF

Data scientist resume example with 8 years of experience

Why this resume works

  • You need to  write your resume  in a way that  shows the employer that you’ve materially impacted the companies you’ve worked for.
  • This means you should quantify your value in terms of business impact, not model performance. Model performance metrics without context really don’t convey much.
  • They’re a way to quickly display your achievements and convince the employer that you’ll bring that same kind of energy to their team or company.

Data Science Student Resume

Data science student resume example with data entry experience

  • For a splendid data science student resume, demonstrate a diverse skill set, prioritizing in-demand options (think Python, Jupyter Notebook, Pandas, Excel, SQL Server, etc.). Soft skills , ranging from teamwork and leadership to problem-solving, creativity, and adaptability are a welcome addition to your piece.

Entry-Level Data Scientist Resume

Entry-level data scientist resume example

  • Considering adding projects to your  entry-level data scientist resume  in lieu of enough work experience?
  • You can demo the punch of a project by framing a question and then answering that question with data.
  • Again, your results should be consistently expressed in numbers. Even if the result is as silly as saving 12 minutes per movie, it recognizes the importance of measuring impact.
  • Customizing looks like: mentioning the target business by name and including relevant keywords from the  job description . 

Associate Data Scientist Resume

Associate data scientist resume example

  • When you have little to no professional background,  the skills you list on your resume  matter more than ever. And your abilities aren’t just selling points—they’re also a springboard for you to demonstrate your willingness to learn. 
  • While writing your associate data scientist resume objective, immediately dive into any education or internship highlights with notable companies like Northrop Grumman. Then, sprinkle in some personality that shows your enthusiasm for new knowledge—drive and inquisitiveness are highly desirable traits in new professionals.

Senior Data Scientist Resume

Senior data scientist resume example with 10+ years of experience

  • Your  senior data scientist resume  can really wow when you show a clear career progression from data analyst to data scientist to senior data scientist.
  • That said, if you’ve got at least four years of experience under your belt, it’s fine for your work experience to account for about 70 percent of the page.
  • A worthwhile summary should give a quick snapshot of your career highlights in two to three power-packed sentences and include the target company by name.

Data Scientist Intern Resume

Data science intern resume example with 1+ years of experience in retail

  • Call attention to your expertise in computer science by listing your proficiency in advanced programs like Keras on your data scientist intern resume.

Data Visualization Resume

Data visualization resume example with 6 years of experience

  • Whether it’s geospatial analysis, real-time data monitoring, or even creating standard visuals, make sure to quantify the impact of each and clearly state the benefit these tasks brought to the company to strengthen your data visualization resume.

Healthcare Data Scientist Resume

Healthcare data scientist resume example with 6 years of experience

  • Having two qualifications! Now’s the time to show all the degrees you’ve got! The best-case scenario is to have two degrees where one caters to the healthcare field while the other highlights your expertise in data science!

Amazon Data Science Resume

Amazon data science resume example with 10+ years of experience

  • Let that statement capture your aspirations and what you desire to bring to your new employer. Hiring managers are eager to see your passionate side and value to the team.

Python Data Scientist Resume

Python data scientist resume example with 10+ years of experience

  • Mentioning achievements such as improving project outcomes and reduction in process duration in your Python data scientist resume is a great way to leverage your experience honed over years of hard work.
  • Then, by writing a great cover letter , you give yourself room to expound on exactly how you reduced process duration as a Python data scientist.

Data Scientist Machine Learning Resume

Data scientist machine learning resume example with 10 years of experience

  • Even if you already have ample experience in your field, you can give your data scientist machine learning resume a competitive edge by bringing your higher education to light. Create space to showcase your advanced degree in a relevant subject like statistics to further stand out.

Data Science Manager Resume

Data science manager resume example with 10+ years of experience

  • Again, the results of your work should be stated clearly in terms of tangible impact (are you sensing a theme?). 
  • Using a two-column layout for your  data science manager resume  allows more information to fit on a single page. Even with nine-plus years of experience, keeping your resume to one page is ideal.
  • Fretting these details? Our  resume templates for 2024  may suit your specific needs; additionally, we’ve got fresh and  free Google Docs resume templates  that can make your  resume-creation  blues go away!

NLP Data Scientist Resume

Nlp data scientist resume example with 7 years of experience

  • When you’re trying to figure out  what to put on your resume  for a more specialized role like an NLP data scientist, it’s important you showcase your proficiency in operationalizing models to have a big impact on the business.
  • Don’t focus on the technical aspects of the models you’ve built on your  NLP data scientist resume  (you’ll talk more about that in your interviews). Instead, take a step back and talk about the broad impact you’ve had in your previous roles.

Metadata Scientist Resume

Metadata scientist resume example with 2+ years of experience

  • Prove your experience in programming, testing, modeling, and data visualization through well-designed projects that solve real problems through code.
  • The key isn’t to reinvent the wheel but to create something dynamic and unique that isn’t easily replicated with a few Google searches and a video tutorial.
  • Solve this problem with projects. If you’ve worked on excellent projects that used and showcased the necessary skills required for the job, list them and watch your resume bloom with confidence!

Educational Data Scientist Resume

Educational data scientist resume example with 10+ years of experience

  • Think “well-rounded” as you write; you might include an exciting publication related to the job role, quickly outline your relevant experience or abilities, and conclude with how and why you’ll better the company through your new role. 
  • Skills and certifications add credibility, but potential employers also want to know about your impact.
  • If you performed evaluations, what improvements did you make afterward? If you integrated machine learning, what optimizations did you use it for?

Data Analytics Scientist Resume

Data analytics scientist resume example with 5 years of experience

  • Your data scientist, analytics resume should target the list of requirements that companies in your state commonly request.
  • For example, 18 out of 20  job descriptions  for data science, analytics in the state of California list Python, SQL, R, Tableau, and Hadoop (in that order) as required skills.
  • After you add job-market-specific data, our  free resume checker  can assess your resume for other key elements like spelling, grammar, and active language. 

Data Science Consultant Resume

Data analytics consultant resume example with 9 years of experience

  • To best represent your capabilities, use metrics to talk about your accomplishments.

Data Science Director Resume

Data science director resume example with 5 years of experience

  • For an effective data science director resume, use a clean and simple resume template and format your work experience in reverse-chronological order. Doing so will put your most recent and relevant accomplishments at the top, making it the first thing a recruiter will look at.

Related resume guides

  • Data Analyst
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Three peers review job application materials on laptop and tablet

Recruiters only spend an  average of seven-plus seconds reviewing your resume , so it’s vitally important that you catch their attention in that time. Our guide for 2024 takes you section by section through your resume to ensure you get that first interview.

You can successfully choose a winning  resume format in 2024  that will snag an employer’s attention.

Short on time? Here are the quick-hit summaries of each section you can apply to your resume:

  • Whether for a company or yourself, what you’ve worked on should be the focus of your resume. Always try to include a measurable impact of your work.
  • Make this the job title you’re looking for (e.g., “data scientist”), and don’t worry about a summary unless you’re making a career change.
  • Only include technical skills that you’d be comfortable having to code with/in during an interview. Avoid a laundry list of different skills.
  • Include relevant courses if you’re looking for an entry-level role. Otherwise, make your work the focus of your resume. If you went to a boot camp, list it here.
  • Double-check everything. This is not the place you want to make a mistake. You don’t need to put your exact address. City, state, and zip are fine.
  • Try to keep it to one page. Keep your bullets brief. Triple-check your grammar and spelling, and then have someone else read it.
  • Read the  data scientist job description . See if any projects you’ve worked on come to mind while reading it. Incorporate those specific projects into your resume.

data science projects to put on resume

Your data science projects and work experience

Let’s jump right into the good stuff and talk about the most important part of your resume: your work experience and projects. This is it. This is the grand finale. This is where the person reviewing your resume decides whether or not you’ll get an interview.

When talking about your previous work (whether that’s for another employer or on a side project), your goal is to convince the person reviewing your resume that you’ll provide value to their company. This is not the place to be humble. We want to see that “I’m wearing my favorite outfit” level of confidence.

The template for successfully talking about your experience as a data scientist is:

  • Clearly state the goal of the project
  • You can mention the programming languages you used, the libraries, modeling techniques, data sources, etc.
  • State the quantitative results of your project

You’re a data scientist, so highlight your value by demonstrating the quantitative impact of your work.  These can be estimates . For example, did you automate a report? Roughly how many hours of manual work did you save each month? Here are some ideas for how you can quantitatively talk about your projects:

Ways to define the impact of your data science work

  • Example:  You developed a pricing algorithm that resulted in a $200k lift in annual revenue.
  • Example:  You built a model to predict who would cancel their subscription and introduced an intervention to improve monthly retention from 90% to 93%.
  • Example:  You built a marketing attribution model that helped the company focus on marketing channels that were working, resulting in 2,100 more users.
  • Example:  You ran an experiment across different product features, which resulted in a 25% increase in engagement rate.
  • Example:  As a side project, you built a movie recommendation engine that now saves you 26 minutes each time you need to decide which movie to watch.
  • Example:  Since you built a customer segmentation model to determine how to communicate with different customer types, customer satisfaction is up 17%.

Numbers draw attention, are convincing, and make your resume more readable. Which of these two ways to describe reporting is more compelling?

  • Used Python, SQL, and Tableau to conduct daily reporting for the business
  • Using Python, SQL, and Tableau, combined 11 data sources into a comprehensive, real-time report that saved 10 hours of work weekly

If nothing else, please take this away from this guide:  state the results of your projects on your resume in numbers.

data science projects to put on resume

Trade-offs between projects and work experience

Simply put, the more work experience you have, the less space “projects” should take up as a section on your resume. In the sample resumes above, you’ll notice that only the more entry-level data scientist resumes have a section for projects.

The senior-level resumes focus on projects in the context of experience within companies. Real estate is precious on a one-page resume, so you’ll want to focus on the bullets that most clearly demonstrate how you’re a great fit for the job. Companies want to hire data scientists who have demonstrated success at other companies.

data science projects to put on resume

Entry-level data science projects for resume

Junior data scientists should include projects on their resumes. Try starting with a  resume outline , where you can brain dump anything and everything about your projects; then, you can distill the best of it into your final resume. Can you share the Github link? Do you have a link to a write-up you did about your project?

The more initiative you can show for entry-level data science projects, the better. Do you have any questions to which you’ve always wanted the answer? You can probably think of some clever ways to get data around that question and come up with a reasonable answer. For example, our co-founder wanted to know  which data science job boards were best , so he pulled together some data, laid out his assumptions and methodology, and made his conclusions.

Sample Data Science Projects

No matter what projects you include on your resume, be sure to clearly state the question you were answering, the tools and technologies you used, the data you used to answer the question, and the quantitative outcome of the project. Succinctly stating conclusions and recommendations from your analysis is a highly sought-after skill by employers in data science.

data science projects to put on resume

The data scientist summary

Since you have limited space on your resume, you should only include a  resume objective  if you take the time to customize it for each role to which you apply.

You may want to include a  resume summary  or objective when you’re making a big career change. If you do include one, make sure to keep it specific about your goal and experience. This is valuable space you’re going to be using on this statement, so take the time to personalize it to each job.

Include the title of the job you’re looking for under your name. This should be aspirational. So if you’re a data analyst looking to apply for data scientist jobs, you would put “data scientist” under your name as the headline:

Sample Data Science Resume Headlines.

Skills that pay the bills

The most common mistake we see on data science resumes (that we used to make on our resumes) is what we call skill vomit. It’s a laundry list of skills in which no one person could have expertise. A quick rule of thumb:  if the skills section takes up a third of the page, it takes too much space. This is a big red flag for hiring managers.

The reason people make such an exhaustive skills section is to get through the mythical data science resume keyword filters. If you’re changing your resume in small ways for each job you apply to (for example, put Python for jobs that mention Python and R for jobs that list R if you know both), you’ll have no problem with those keyword filters.

The rule of thumb that we recommend you use in determining whether to include a skill on your resume is this:  i f it’s on your resume, you should be comfortable coding with/in it during an interview.

So that means if you’ve read a few articles on Spark or adversarial learning, but you can’t use them in code, they should not be on your resume. If you only have a handful of tools under your toolbelt, but you can use them effectively to answer questions with data, you’ll be able to find jobs looking for that skill set. 

We can assure you there are all kinds of data science jobs available. Our scraper that indexes jobs across thousands of company websites shows over 5,000+ full-time data science job openings in the US across all tenures and skill sets. And our scraper has a lot of room for improvement, so that’s significantly lower than the actual number. 

There are tons of fish in the job market sea; you just need a fishing rod.

data science projects to put on resume

Entry-level vs. senior skills sections

Generally, the more senior you are, the shorter your skills section needs to be. If you’re a senior data scientist, you should talk about the major tools and languages you use but save specific modeling techniques for the “Work Experience” section. Show how you used particular models in the context of your work.

When you’re more junior, you likely haven’t had the chance to use all of the techniques you’re comfortable with within work or a project. That’s okay! It’s expected. But you still want to make it clear to a potential employer that you can use those methods or libraries.

Example Data Science Skills Section.

Education is a lot like skills in that the more senior you are as a data scientist, the less space the education section should take up on your resume. When you’re looking for one of your first data science jobs, you might want to include courses relative to data science to demonstrate you have a strong foundation.

Classes in subjects like linear algebra, calculus, probability, and statistics and any programming classes are directly relevant to being a data scientist. If you’re looking for your first job out of college, you should include your GPA on your resume. When you have a few years of work experience, it’s not necessary to include it.

If you just finished (or are finishing) a data science boot camp, this is the place to list where you went. You can include the relevant lessons or classes you took. Be sure to have a few projects from your boot camp (especially if it was an original project) in your resume’s “Projects” section.

Sample Data Science Education Section.

Contact information

The takeaway from this section is simple:  this is not where you should make a mistake . Storytime! When our co-founder was first applying to jobs out of college, he realized about 20 applications in, he had spelled his name “Stepen” instead of “Stephen.” Don’t pull a Stepen.

Data suggests that when your email is wrong, your response rate from companies drops to zero percent. That’s just math. We’ve seen exactly four data science resumes where the email address on the resume was incorrect.

Make sure your email address is appropriate. While we don’t doubt the authenticity of your “ [email protected] ” email, maybe don’t use it when applying for jobs. To play it safe, stick to a combination of your name and numbers for your email.

This is the section you can include anything you want to show off for a data science role. Have a blog where you document the analysis you do for Dungeons & Dragons? Active on Github or an open-source project? Include a link to anything relevant to data that will help you stand out in your application.

data science projects to put on resume

General resume formatting tips

This section is just a list of one-off styling and formatting tips for your data science resume:

  • Keep it brief. Bullets should be informative but should not drag on for paragraphs.
  • Each bullet point in your resume should be a complete thought. You don’t have to have periods at the end of each bullet.
  • Keep your tense consistent. If you’re referring to old projects in the past tense, do that for all old projects.
  • Please, please don’t get your contact information wrong.
  • Don’t give the person reviewing your resume a silly reason to put it in the “No” pile.  Check your resume  carefully.

data science projects to put on resume

Customization for each application

You don’t have to go overboard with your resume customization. Here are the steps we recommend to customize it for each job:

  • So in this example, we’ll have one “Python” resume and one “R” resume depending on what the job is seeking.
  • For example, if you have experience with attribution modeling and this is a marketing data science role, you should include that experience.
  • Do you have experience with a certain library or modeling technique they mention? 
  • Do you have experience in the domain of the specific job?
  • Do you have any relevant industry experience with the company?

Let’s walk through a specific example to highlight what we mean by including particular projects for different jobs. Let’s say that a senior data scientist is applying for the position below.

Sample Data Science Job Description.

In the “Ideally, you’d have” section, they mention they want someone who has “Experience with ETL tools.” Let’s say that in reality, the candidate had a large role in building out data pipelines in his fictional role as a senior data scientist at EdTech Company.

So all we’d do is change that section of his experience at EdTech Company to talk about that project, as you see below:

Data science resume customization example

Original bullet on the resume: Worked closely with the product team to build a production recommendation engine in Python that improved the average length on the page for users and resulted in $325k in incremental annual revenue

Customized for the role: Built out our company’s ETL pipeline with Airflow, which scaled to handle millions of concurrent users with robust alerting/ monitoring

data science projects to put on resume

Customization for startups

For early-stage startups (anything less than 50 employees), one of the most important qualities they’re looking for in a hire is ownership. That means they want someone who can ask a question and come up with an answer with minimal instruction. 

If you want to stand out to these companies, you should demonstrate ownership in the way you list projects on your resume. Include active words like “drove” or “built” instead of passive language like “worked on” or “collaborated on.” We know this seems nit-picky, but this matters to early-stage companies. Hiring managers at companies this size are strained for time and will use any signal to weed people out.

Concluding thoughts

There you have it—a compelling, easy-to-read data science resume built for 2024. Now you can celebrate by doing something as fun as  writing a resume . Maybe your taxes? Or go to the dentist?

By building or  updating your current resume , you took a huge step toward landing your next (or first) data science job. Now please, we beg you, check your grammar and spelling again and have someone else read your resume. Don’t let that be the reason you don’t get an interview.

Congrats! The first and hardest step is done. You have a data science resume! With great power comes great responsibility, so go and apply wisely.

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Top Data Science Projects With Source Code

Data science project ideas, best data science projects for beginners, intermediate data science projects with source code, advanced data science projects with source code, additional resources.

Data Science continues to grow in popularity as a promising career path for this era. It’s one of the most exciting and attractive options available. Demand for Data Scientists is increasing in the market. According to recent reports, demand will skyrocket in the future years, increasing by many times. Data Science encompasses a wide range of scientific methods, procedures, techniques, and information retrieval systems to detect meaningful patterns in organized and unstructured data. More opportunities emerge in the market as more industries recognize the value of Data Science. 

If you’re interested in Data Science and want to learn more about the technology, now is as good a time as ever to develop your abilities to understand and manage the upcoming problems. Initially, understanding it can be difficult, but with regular effort, you will soon understand the many concepts and terminology used in the field. If you are interested in becoming a Data Scientist , it is strongly recommended that you apply your skills to become a competent professional in this sector. If you’re genuinely interested in learning what it’s like to be a professional after gaining some solid theoretical understanding of Data Science, now is the time to start working on some actual projects. 

As a result, participating in live Data Science Projects will enhance your confidence, technical expertise, and general confidence. But, most significantly, if you undertake Data Science projects for final year projects, you will find it much simpler to land a solid job.

Confused about your next job?

This article aims to give project ideas on data science that are appropriate for different levels of learners.

 This section will provide a list of data science project ideas for students new to Python or data science in general. These data science projects in python ideas will provide you with all of the tools you’ll need to succeed as a data science developer . The following are the data science project ideas with source code.

1. Fake News Detection Using Python

Fake news do not require any introduction. It is very much easy to spread all the fake information in today’s all-connected world across the internet. Fake news is sometimes transmitted through the internet by some unauthorised sources, which creates issues for the targeted person and it makes them panic and leads to even violence. To combat the spread of fake news, it’s critical to determine the information’s legitimacy, which this Data Science project can help with. To do so, Python can be used, and a model is created using TfidfVectorizer. PassiveAggressiveClassifier can be implemented to distinguish between true and fake news. Pandas, NumPy, and sci-kit-learn are some Python packages suitable for this project, and we can utilize News.csv for the dataset.

Source Code – Fake news detection using python

2. Data Science Project on Detecting Forest Fire

Developing a project for identifying the forest fire and wildfire system is an alternatively good example to exhibit one’s skills in Data Scienc e. The forest fire or wildfire is an uncontrollable fire that develops in a forest. All the  forest fir will create havoc during weekends on the animal habitat, surrounding environment and human property. k-means clustering can be used for the identification of the  crucial hotspots during forest fire  and to reduce the  severity , to regulate them and even  to predict the behaviour of the wildfire. This is advantageous for allocating the required resources. To enhance the model’s accuracy, it is ideal to use climatological data to find out the common periods and seasons for wildfires.

Source Code – Detecting Forest Fire

3. Detection of Road Lane Lines  

A Live Lane-Line Detection Systems built-in Python language is another Data Science project idea for beginners. A human driver receives lane detecting instruction from lines placed on the road in this project. The lines placed on the roads indicate where the lanes are located for human driving. It also refers to the vehicle’s steering direction. This application is crucial for the development of self-driving cars. This application for the Data Science Project is critical for the development of self-driving cars.

Source Code – Detection of Road Lane Lines

4. Project on Sentimental Analysis

The act of evaluating words to determine sentiments and opinions that may be positive or negative in polarity is known as sentimental analysis. This is a sort of categorization in which the classifications are either binary (optimistic or pessimistic) or multiple (happy, angry, sad, disgusted, etc.). The project is written R Language, and u the dataset provided by the Janeausten R package is used. The general-purpose lexicons like AFINN, bing, and Loughran are used to execute an inner join and present the results using a word cloud.

Source Code – Project on Sentimental Analysis

5. Project on Influences of Climatic Pattern on the food chain supply globally

The abnormalities and changes occurring in the climate very often are the main challenges impressed on the environment that needs to be taken care of. These environmental changes will affect the human beings on earth. This Data Science Project makes an attempt to analyse the changes in the food production globally that occurs due to change in climatic conditions. The main purpose of this study is to evaluate the consequences of climatic changes on primary agricultural yields. This project will evaluate all the effects related to change in temperature and rainfall pattern. The amount of carbon dioxide that impacts plant development and the uncertainties in climate change will next be considered. As a result, data representations will be the primary focus of this project. It will also assess productivity across different locations and geographical regions.

In this section, data science projects for intermediate level learners are discussed:

1. Project on  Speech Recognition through the Emotions

One of the fundamental strategies for us to communicate ourselves is the speech, and it involves various feelings including silence, anger, happiness, and passion etc. It is possible to use the emotions behind the speech to reorganize our emotions, the service we offer, and the end products to deliver a custom-made service to particular persons by evaluating the emotions behind it. The main aim of this project is to identify and get the feelings from multiple files involving sound that comprises the human speech. Python’s SoundFile, Librosa,, NumPy, Scikit-learn, and PyAaudio packages can be used to produce something alike. In addition, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for the dataset containing over 7300 files.

Source Code – Speech Emotion Analyzer and Speech Emotion Recognition

2. Project on Gender Detection and Age Prediction 

This project on detecting the gender and predicting the age identified as a classification challenge, will put your Machine Learning and Computer Vision skills to work. The goal is to create a system that can analyze a person’s photograph and determine their age and gender. Python and the OpenCV library to implement Convolutional Neural Networks can be used for this entertaining project. For this project, the Adience dataset can be downloaded. Remember that factors like cosmetics, lighting, and facial expressions will make this difficult, and try to throw your model off.

Source Code – Gender Detection and Age Prediction

3. Project on Developing Chatbots

Chatbots are important for companies since this project can answer all the questions posed by the clients and information without the process being slowing down. The customer support workload has been decreased by the procedures which is fully automating. This process can be easily obtained by implementing Machine Learning,  Artificial Intelligence and Data Science techniques. Chatbots operate by assessing the customer’s input and responding with a mapped response. Recurrent Neural Networks using the intentions JSON dataset may be used to train the chatbot, while Python can be used to implement it. The objective of the chatbot will determine whether it is domain-specific or open-domain.

Source Code – Developing Chatbots

4. Project on Detection of Drowsiness in Drivers

Sleepy drivers are one of the causes of road accidents, which claim many fatalities each year. Because drowsiness is a possible cause of road danger, one of the best methods to avoid it is to install a drowsiness detection system. Another technology that can save many lives is a driver sleepiness detection system that continuously assesses the driver’s eyes and alerts him with alarms if the system detects that the driver closes his eyes very often. A webcam is required for this project for the system to monitor the driver’s eyes regularly. This Python project will require a deep learning model as well as packages such as OpenCV, TensorFlow, Pygame, and Keras to do this.

Source Code – Driver Drowsiness Detection and Driver Drowsiness Detection

5. Project on Diabetic Retinopathy

Diabetic Retinopathy is a primary cause of blindness in people with diabetes. An automated diabetic retinopathy screening system can be developed. On retina photographs of both damaged and healthy people, a neural network can be trained. This research will determine whether or not the patient has retinopathy.

Source Code – Diabetic Retinopathy Detection and Diabetic Retinopathy Detection Topics

In this section, the data science projects for advanced learners are discussed.

1. Project on Detection of Credit Card Fraud

Credit card fraud is more widespread than you might believe, and it’s been on the rise recently. By the end of 2022, we’ll have crossed a billion credit card users, metaphorically. However, credit card firms have been able to successfully identify and intercept these frauds with significant accuracy because of advancements in technology such as Artificial Intelligence, Machine Learning, and Data Science . Simply stated, the concept is to examine a customer’s regular spending pattern, involving locating the geography of such spendings, to distinguish between fraudulent and non-fraudulent transactions. The languages R or Python can be used to ingest the customer’s recent transactions as a dataset into decision trees, Artificial Neural Networks, and Logistic Regression for this project. The system’s overall accuracy would increases if additional data is fed.

Source Code – Credit Card Fraud Detection and Credit Card Fraud Topics

2. Project on Customer Segmentations

One of the most well-known Data Science projects is customer segmentation. Companies build various groupings of customers before launching any marketing. Customer segmentation is a prominent unsupervised learning application. Companies utilize clustering to discover client groupings and target the possible user base. They classify clients based on shared traits such as gender, age, interests, and spending habits to market to each group successfully. Visualization of the gender and age distributions can be done using K-means clustering. Then their annual earnings and spending habits are also analyzed.

Source Code – Customer Segmentations and Customer Segmentations Topics

3. Project on the recognition of traffic signals

Traffic signs and rules are extremely crucial to observe to avoid any accidents. To observe the guideline, one must first comprehend the appearance of the traffic sign. Before receiving a driver’s license, a person must first study all of the traffic signs. However, automated vehicles are on the rise, and in the not-too-distant future, there will be no human drivers. In the Traffic Signs Recognition project, you’ll discover how software can use a picture as input to recognize the type of traffic sign. The German Traffic Signs Recognition Benchmark dataset (GTSRB) is used to train a Deep Neural Network that can identify the class of a traffic sign. A simple graphical user interface (GUI) to communicate with the application can also be created. Python can be used.

Source Code – Traffic Sign Detection , Traffic Sign Detection Using Capsule Networks , and Traffic Sign Recognition

4.Project on recommendation System for Films

In this data science project, the language R can be used to generate a machine learning-based movie recommendation. A recommendation system uses a filtering procedure to send forth suggestions to users based on other users’ interests and browsing history. If A and B enjoy Home Alone and B enjoys Mean Girls, it can be recommended to A; they may enjoy it as well. Customers will be more engaged with the platform as a result of this.

Source Code – Recommendation System for Films

5. Project on Breast Cancer Classification

Breast cancer cases have been on the rise in recent years, and the best approach to combat it is to detect it early and adopt appropriate preventive measures. To develop such a system with Python, the model can be trained on the IDC(Invasive Ductal Carcinoma) dataset, which provides histology images for cancer-inducing malignant cells. Convolutional Neural Networks are better suited for this project, and NumPy, OpenCV, TensorFlow, Keras, sci-kit-learn, and Matplotlib are among the Python libraries that can be utilized.

Source Code – Breast Cancer Risk Prediction , Breast Cancer Classification , and Breast Cancer Classification Topics

A thorough insight about data science, its importance, and the data science projects for beginners and final years are discussed. All of these data science projects’ source code is available on Github. So get started right away and create a Data Science project. Follow the steps from beginner to advanced, and then move on to other projects.

Q. How do you get ideas for data science projects?

The ideas for data science projects can be obtained by following these simple tips:

  • Attending networking events and mingle with people.
  • Make use of your interests and hobbies to come up with new ideas.
  • In your day job, solve problems
  • Get to know the data science toolbox.
  • Make your data science solutions.

Q. What projects do data scientists work on?

There are four different types of projects on which data scientists work:

  • Projects to cleanse up data
  • Projects involving exploratory data analysis.
  • Projects involving data visualization
  • Projects involving machine learning

Q. What projects can I do with R?

The following are the list of projects that can be done using R:

  • Project on Sentiment Analysis 
  • Project on Uber data analysis
  • Project on Movie recommendation systems
  • Project on Customer segmentation
  • Project on Credit card fraud detection
  • Project on wine preference prediction

Q. How do you contribute to open source data science projects?

There are numerous motivations to contribute to an open-source project, including:

  • To make the software, you use every day better
  • If you require a mentor, you should look for one.
  • to get creative knowledge
  • to demonstrate your abilities
  • To learn a lot more about the software you’re working with
  • To improve your reputation and advance your career

Q. How do I start a data science from scratch?

To start the data science journey from scratch, you should follow these steps mentioned below:

  • Learn Python
  • Learn the fundamentals of statistics and mathematics
  • Learn Data analysis using Python
  • Learn machine learning and start doing projects

Q.  How do you put a data science project on your resume?

Projects can be stated as accomplishments below a job description on a resume. Projects, Personal Projects, and Academic Projects can all be listed in a distinct section. Academic work should be listed in the education portion of the resume. You can also make a CV that is focused on a certain project.

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Data Science Projects for Boosting Your Resume

Peter Scobas

Peter Scobas

  • June 28, 2019

We all know the old catch-22 — you need a job to get job experience and job experience to get a job. Luckily, that’s not entirely true in data science . You can use personal data science projects to demonstrate your skills to prospective employers — especially for landing your first data science job.

But where do you start? It’s important to pick a project you can showcase effectively. And it’s just as important to know how to include it in your resume or CV.

When you’re just starting to look into putting together your own data science project, you might feel a bit overwhelmed. In this post, I’ll guide you through the data science personal project process — from how to pick a good project topic to how to actually utilize your data science projects in your application.

data science project guestpost Peter Scobas

What to think about before picking a data science project topic

Before you start brainstorming topics, it’s important to think about the point of these projects: to show prospective employers you have strong technical skills and a knack for presenting data science results.

During a standard application process, you really have two opportunities to show and discuss your projects to the hiring team: a non-conversational opportunity (so either on your resume/CV or on your personal website — more on this later) as well as during an actual interview.

You need your project topic to work well in both capacities. Is it easy to digest and is it skimmable, so a recruiter or a hiring manager can quickly read it and understand it? Can you elaborate and discuss it at length to an interviewer?

So you might be thinking — wait, skimmable? I’m doing a bunch of work so a recruiter or a hiring manager might skim my data science project?

It’s true. The reality is that (at least during the early stages of the job application process) your application will be skimmed. And this includes your personal projects. Now, if a project catches their eye, a recruiter or hiring manager will spend more time reviewing your work. Which brings me to my next point: pick a project topic that will make potential recruiters and hiring managers say, “Huh. That’s actually pretty cool.”

Lastly: how many projects do you really need? I personally believe 2-3 good, interesting side projects is more than enough . Hiring companies just won’t spend the time looking through and reading the 4, 5, 6+ projects you have.

How to pick your dataset

The process of brainstorming your project topic starts off fairly straightforward. I recommend you begin by Googling “free public data” to get a general idea of what data is out there (or visit Google’s dataset search feature ) — and what you might be interested in working with. (Spoiler: there are TONS and TONS of free public datasets out there). 

data science project google public dataset

Before getting into data science, I came from an economics research background — so I knew a ton about where to find and how to analyze U.S. economic data. For one of my projects, I experimented with R’s ggplot2 and created aesthetically-pleasing charts to show economic trends using data from the Federal Reserve’s Economic Database . I was able to explain this project during one of my interviews because the panel was impressed by the visualizations I constructed… Moral of the story: companies are impressed when you have a portfolio of projects. And personal projects give you the chance to discuss work that you know a lot about and are passionate about.

If you’re still struggling for inspiration, a great strategy is finding a way to weave together data and pop culture. I’m a huge T.V. comedy fan; one of my favorite shows of all time is Parks and Recreation . It’s fairly easy to take one of your favorite shows or movies, find the script online, scrape the show/movie dialogue, and do some basic text analysis. If you’re intrigued with blending data science and pop culture but need more inspiration, I highly recommend the website Pudding.cool . (It’s also just a fantastic website to browse.)

Okay, so to summarize: start by thinking about a topic that you’re interested in. Google “free public data” if you need some inspiration–and don’t be afraid to get creative!

How to decide what to analyze  

Once you’ve decided on a dataset you’d like to explore, the next step is actually figuring out what questions to answer and what to analyze. If you recall what I said earlier: the best data science personal projects are eye-catching and skimmable. And the easiest way to make them that way is to create an awesome visualization.

No matter what you analyze, what question you try to answer, or what methodology you use, you need to think about how you will visualize your results. When you’re exploring your dataset, start thinking about possible trends or different ways you can segment the data.

Let’s revisit my Parks and Recreation example from before. Using the show dialogue, you can create a visualization to see which characters had the most lines. Or find out (if you’re familiar with the show this will make more sense) were Leslie Knope and the rest of the Parks Department really that mean to Jerry?

You might feel like you need to shoot for the moon and put together some technically-astounding machine learning project in order to impress a hiring team. If you have a strong background in statistics and programming and a lot of time — more power to you. However, a project like this is in no way necessary for getting hired as a data scientist. This may be a subject for another blog post, but in my experience, aspiring data scientists seem to immediately jump to fancy machine learning or deep learning tutorials — and forget about learning the basics and honing their problem solving, critical thinking, and presentation skills. 

If you’d like to go for an in-depth machine learning project — that’s great. But if you don’t, rest assured that simply answering an interesting and insightful question with your dataset is more than enough.

How to start building your projects

Once you have settled on how you will analyze your dataset, the next step is to start coding. What’s most important here is writing clean, easy to read, and well-commented code . (This is good practice in general–but especially important for your data science projects.) 

Once your code is written, the best way to display your code (and demonstrate to prospective employers that you can code) is to set up a GitHub account.

Already have a GitHub? Awesome. Just pin the repos you want people to see and add clear and concise READMEs that explain what your project is about.

Don’t have a GitHub? Confused what “pin the repo” means? Then I recommend you create a GitHub account and read this introduction .

GitHub is a fantastic place to demonstrate your programming ability to hiring managers. Just make sure that in addition to having clean and well-commented code, you also include a README file explaining your motivation and what your project is about.

How to present your projects in your CV/resume

Let me just mention this one more time: the point of these projects is to show prospective employers you have strong technical skills and a knack for presenting data science results.

With that in mind, let’s revisit my Parks and Recreation example and I’ll show you how I’d present this project on my resume/CV:




This project is an analysis of my favorite T.V. show, “Parks and Recreation.” I used R’s ggplot2 to construct the visualizations and Python’s BeautifulSoup to scrape the show dialogue.

Okay, so a couple of things to notice: one, yes, this is short. However, space on your resume is scarce. You have your job experience, skills, education, and contact information taking up space. If you’re discussing 2-3 projects (with 1-2 bullet points each), that can easily take up over a third of your resume (and your resume needs to stay one page, of course!). 

Also a topic for another blog post — but you don’t want your resume to become cluttered. More is not always better — short and skimmable is the name of the game.

It’s also important to notice that I mention the packages I used in my project. This signals your programming proficiency and gives recruiters keywords to see. (Oftentimes, recruiters are looking for certain keywords while reviewing resumes.)

Yes, this description is short, and yes it’s disappointing to do a bunch of work and not be able to fully explain and outline your project on your resume. But you have two more opportunities to go more in depth about your projects: on your website and during an actual interview.

In an ideal world, recruiters and whoever else is reviewing your resume would spend 5-10 minutes looking over your resume, carefully reading each bullet point, and fully grasping your skills and experience. However, that’s just not the case. Your resume/CV will be skimmed. Oftentimes, the people who are able to succinctly demonstrate their skills and experience end up getting the interviews. So, write short descriptions. Include keywords. Avoid clutter.

How to present your projects on your website

Your website gives you the opportunity to showcase your personal projects in depth.

As I mentioned before, the best projects to display are ones that can be succinctly presented — meaning, you have a well-constructed plot or table and a clear description of the project that is a few sentences to a paragraph or so in length. Also — don’t forget to include a link to your code!

Below is how I’d present my Parks and Recreation example on my website (note: this is just an example, not an actual analysis of the show) :

data science project chart

At this point you’re probably tired of listening to me say how you need your analysis to be clear and concise. But this point is incredibly important! The biggest struggle with data science departments is being able to effectively communicate their findings to the rest of the company to help make data-driven business decisions. If you’re able to show the hiring manager that you can clearly present your analysis (whether it is a simple visualization or a fancy machine learning model) you will stand out in the interview process.

I’ve always been one to preach simplicity and clarity over anything else — especially for your first data science job. Unless you’re coming from a technical PhD program, companies just aren’t expecting first-time data science applicants to be able to take on difficult machine learning tasks (if a company does expect that from a first-time data science applicant, that company’s data team is a mess).

Your personal data science projects are a fantastic way to showcase your technical skills, presentation skills, and creativity. If you focus on writing clean code and having clear visualizations and an insightful analysis you’ll be well on your way to landing your first data science job.

data science project guestpost peter scobas-1

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Data science projects for resumes

Are you wondering whether you should work on a data science side project to enhance your resume? Or maybe you have already decided that you want to work on a side project, but you are looking for advice on what type of project you should pursue? Either way, we have the answers that you are looking for!  In this article, we discuss everything you need to know about data science side projects and the role they play in enhancing your resume. 

We start off by explaining why data science projects are useful for resume building. After that, we walk through the steps you need to take to build out your projects and give pointers on where to focus your attention. Finally we discuss what types of applicants benefit most from having data science side projects on their resumes.  The advice provided in this  article  is broad enough that it is  applicable  for all data professionals ranging  from  data analysts to machine learning engineers. 

Competencies you might want to display when woking on data science projects for resumes.

Why work on data science side projects

  • Add new skills to your resume . The first reason that you should work on data science side projects and build out a data science portfolio is to learn new skills. Are you an analyst who primarily works in R but is looking to transition to Python? Are you a data scientist who wants to be able to put time series analysis on your resume? There is no better way to learn new skills than to dive in and get hands-on experience. Once you feel comfortable with the new tool, you can add it to the skills section of your resume. 
  • Demonstrate competencies with real examples . Beyond just being able to add new skills to your resume, the main reason that having side projects listed on your resume is impactful is because you can provide actual code and documentation that proves that you do have the skills listed in your resume. Providing links to complex Python projects you have created with real code is much more persuasive than just saying that you would rate yourself as an advanced Python coder. 
  • Prove that you are an independent learner . Finally, having side projects on your resume demonstrates that you are able to learn independently and you are eager to learn new skills. These are  qualities  that hiring  managers  look for, particularly in more junior candidates and career changers. 

Data science competencies for resumes

So what kind of competencies can you demonstrate on your resume using data science projects? Here are some examples of competencies you can demonstrate using side projects. 

  • Data analysis & visualization . The first competency that data science projects and portfolios can help to demonstrate is general data analysis and data visualization skills. If you want to focus on this competency, you should focus on defining good metrics, checking data integrity, and creating beautiful plots that make complex concepts easy to digest. 
  • Machine learning & statistics . A second competency that you can demonstrate by including data science projects on your resume is machine learning and statistics. Whether you want to demonstrate your proficiency in hypothesis testing or learn more about deep learning, all you need to do is choose an appropriate dataset and code up an analysis . If you are looking for a little bit of a challenge, try working on a project that involves time series, network, text, or image data. 
  • Software engineering . A third competency you can demonstrate with data science projects is software engineering skills. If you want to show off your software engineering chops, you do not necessarily need to work on a project that involves complex machine learning models. Just focus on writing well structured,  modular code that is  version  controlled and  well tested.
  • Languages & tools . Finally, if you want to demonstrate your proficiency with a certain language or tool then you can do that with data science projects on your resume. Some common examples of tools that you can demonstrate your proficiency in with data science projects are Python, R, Java, Spark, SQL, Git, Mlflow, Docker, Flask, Pytorch, Tensorflow, AWS, and CI/CD tools. 

Building data science projects for resumes

What steps do you need to go through in order to create a data science project for your resume? Here are the steps you need to go through to build a data science project for your resume. 

  • Decide what competencies to focus on . This is probably the most important step of the process. Before you work on a data science side project for your resume, you should make sure to decide what specific competencies you want to demonstrate with your project. Most people do not put much thought into this step of the process, but the competencies you choose should inform the dataset that you choose and the type analysis you run, not the other way around.
  • Data analysis & visualization . If you want to demonstrate your competency in data analysis and visualization then you are better off picking a real world dataset that is not perfectly clean. This way you can demonstrate your ability to identify issues with data quality and clean data. You should also think about what visualizations you might want to produce and choose your data set accordingly. For example, if you want to create a heat map that shows geographical trends in data then you should make sure to choose a dataset with geographical variables. 
  • Machine learning & statistics . If you want to demonstrate your capabilities with machine learning and statistics, then you should think about what kind of modeling you want to do. If you are new to the field, then we recommend choosing a tabular dataset that has simple numeric and categorical variables. If you do choose to work with a tabular dataset, we recommend choosing a real world dataset that needs some cleaning. If you have already done a project with tabular data and want to learn something new, you can look for unstructured data like text or image data. 
  • Software engineering . If you want to shop off your software engineering skills then it is not as important to find a messy dataset that needs a lot of cleaning. In fact, it may be better to use a clean dataset so that you can focus more of your effort on writing clean code and using model deployment tools. 
  • Languages & tools . If you want to show off your proficiency in a specific language or tool, the type of dataset you want will depend on the kind of tool you want to use. If you want to show off your proficiency using Python and pandas to manipulate data then you should choose a messy real world dataset. If you want to get practice using flask for model deployment then you are in the clear to use a clean, pre-sanitized dataset. 
  • Find a question to answer . After you choose the dataset you want to work with, you need to find a question to answer with your data. Again, the competencies that you are focusing on should inform the type of question you want to ask.  If you want to demonstrate your competencies in software engineering or a process-related tool then the question you ask is not as important. In this case, it is okay to use a dataset that has an obvious question associated with it and just answer that obvious question (ex. the titanic dataset where the obvious question is whether a passenger  lived  or died). If you want to demonstrate your competency in data analysis or modeling tabular data, you should try choosing a unique question that you thought of yourself. This demonstrates that you have the data awareness to be able to look at a dataset and determine what interesting questions can be answered with that data. The question you choose  should provide valuable and actionable insights to either yourself or a hypothetical company that might work with this kind of data. 
  • Analyze the data . After you choose a question to answer, it is time to analyze the data and answer your question. This step will look different for every project so we will not go into too much detail here. 
  • Document your process . After you have answered your question, you should document your process. This is a step that is sometimes overlooked, but it is very important. Hiring managers will not spend a long time looking at your personal projects, so it needs to be clear to them from a glance what each project is and what competencies you are trying to prove. At bare minimum, you should write up a short introduction that clearly states what dataset you are using, what question you are answering, why the answer to that question provides value (if applicable), and what competencies you are demonstrating with this project. Do not just assume that hiring managers will browse through your project and see that you are trying to demonstrate your proficiency in a certain area. Specifically stating the competencies you are trying to demonstrate will help them determine what parts of your code and analysis to focus on. 

Who are data science projects most useful for?

Having data science projects on a resume will be more helpful for some types of candidates than others. So what groups of people can benefit most from having data science projects on their resume? 

  • Junior candidates . Data science projects on resumes are generally most helpful for junior to mid level candidates where there is more of an emphasis on technical skills and execution. As candidates become more senior, there is more emphasis on interpersonal skills that are not as easy to demonstrate with data science projects on resumes. Additionally, more senior candidates are likely to have more work-related projects on their resumes that they can talk about so they do not benefit as much from having side projects on their resumes. This is not to say that data science projects are not useful for more senior candidates, especially candidates that are aiming to demonstrate highly specialized skills. Junior and entry level candidates that do not have many work-related projects on their resumes will just get more bang for their buck. 
  • Career changers . Data science projects on resumes are also useful if you are in the process of changing careers or fields. Even if you are just trying to make a small jump from an analytics role where you mostly work on reporting and metric definition to a role that involves more machine learning and modeling, side projects can provide you with valuable hands-on experience with new tools that you may not have the opportunity to use at your day jobs. 

Where to display data science projects

Where should you display your data science projects after you have completed them? Here is some advice on where to display your data science projects.

  • On your resume . Of course if you are working on data science projects with the intention of enhancing your resume, you should display your data science projects on your resume. In general, we recommend having a separate section for side projects called something like “personal projects” rather than lumping your projects into a general experience section.  But how much room should you dedicate to personal projects? That depends on what previous experience you have and whether you have work-related projects that demonstrate your data science skills. If you do not have many work-related projects to show off, then you can include a few bullet points per project for the personal projects on your resume. If you have a few work-related projects and you are not changing fields then we recommend only including one high level bullet point per project to leave more room for your work projects. 
  • Github . Beyond listing your projects on your resume, you should also make your code available in a publicly available repository. The easiest way to do this is to upload your code to GitHub. Along with your code, you should upload a file that describes your project and what its goals were. 
  • Personal website . If you have a personal website, then you may choose to make your code and documentation available there rather than on GitHub. 

Tips for data science projects on resumes

What other tips do we have for creating data science projects for resumes? Here are all of the points we haven’t touched on. 

  • It is okay to use school projects . If you are an entry level candidate, it is okay to use projects that you completed in school in your portfolio of data science projects. You already did the work, so you might as well reap some of the rewards. 
  • Navigation and documentation need to be clear . If you are including a link to a public GitHub profile that has a lot of repositories, make sure it is clear which repositories you want hiring managers to look at. Make sure to highlight those repositories and include README files that clearly describe the project and its importance. 
  • Quality over quantity . As with many things in life, you should aim for quality over quantity when you are working on data science projects for resumes. You are better off having one clean, completed, well documented project than a handful of half-completed projects with no documentation. Consider setting GitHub repositories containing half-completed projects to private when you are applying to jobs. 
  • Emphasize data over models . Even if you are working on projects to demonstrate your competency in machine learning and statistical modeling, you should spend more time focusing on your data than your models. For most jobs, you are better off using a simple, stable model that can be easily maintained than using a more complicated model that has 0.1% better accuracy. Let your projects reflect this type of thinking. And even if tiny increases in accuracy are to be desired, there is often more to gain from adding new data and features to your model than testing hundreds of parameter combinations. 

Have any other questions?

Feel free to leave us a comment if you have any general questions about creating data science projects to enhance your resume and build your skillset. 

If you are looking for a mentor to assist you with building a data science project for your resume, feel free to reach out to us at [email protected]! We can help you select an idea for your project, plan out a roadmap, and find solutions for difficult problems that are blocking your progress.  Note that we charge an hourly personal career consulting rate for these services. 

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Top 65+ Data Science Projects in 2024 [with Source Code]

Data Science Projects involve using data to solve real-world problems and find new solutions. They are great for beginners who want to add work to their resume , especially if you’re a final-year student . Data Science is a hot career in 2024, and by building data science projects you can start to gain industry insights.

Think about predicting movie ratings or analyzing trends in social media posts. For example, you could guess how people will rate movies or see what’s popular on social media. Data Science Projects are a great way to learn and show your skills, setting you up for success in the future.

Data-Sciecne-Projects

Explore cutting-edge data science projects with complete source code for 2024. These top Data Science Projects cover a range of applications, from machine learning and predictive analytics to natural language processing and computer vision . Dive into real-world examples to enhance your skills and understanding of data science.

Table of Content

What is Data Science?

Why build data science projects, best data science projects with source code, top data science projects – faqs.

Data Science is all about making sense of big piles of data . It’s like finding patterns and predicting future outcomes based on data. Data scientists use special tools and tricks to turn huge data into helpful information that can solve problems or make predictions.

Data Science is like being a detective for numbers. It’s about digging into huge piles of data to find hidden treasures of insights. Just like Sherlock Holmes uses clues to solve mysteries, data scientists use algorithms and techniques to uncover valuable information that helps businesses make better decisions.

Data Science Projects are important because they help us make better decisions using data . Whether it’s predicting trends in finance , understanding customer behavior in marketing, or diagnosing diseases in healthcare, data science projects enable us to uncover insights that lead to smarter choices and more efficient processes.

Data Science projects are like powerful tools that help us understand the world around us. They let us see patterns in data that we wouldn’t notice otherwise. By using these patterns, we can make smarter decisions in everything from business to healthcare, making our lives better and more efficient.

Let us look at some fun and exciting data science projects with source codes, that you can build.

Here are the best Data Science Projects with source code for beginners and experts to give a great learning experience. These projects help you understand the applications of data science by providing real world problems and solutions.

These projects use various technologies like Pandas , Matplotlib , Scikit-learn , TensorFlow , and many more. Deep learning projects commonly use TensorFlow and PyTorch, while NLP projects leverage NLTK, SpaCy, and TensorFlow.

We have categorized these projects into 6 categories. This will help you understand data science and it’s uses in different field. You can specialize in a particular field or build a diverse portfolio for job hunting.

Top Data Science Project Categories

Web scraping projects.

  • Data Analysis and Visualization Projects

Machine Learning Projects

  • Time Series Forecasting Projects

Deep Learning Projects

Opencv projects, nlp projects.

Explore the fascinating world of web scraping by building these data science projects with these exciting examples.

  • Quote Scraping
  • Wikipedia Text Scraping and cleaning
  • Movies Review Scraping And Analysis
  • Product Price Scraping and Analysis
  • News Scraping and Analysis
  • Real Estate Property Scraping and visualization
  • Geeksforgeeks Job Portal Web Scraping for Job Search
  • YouTube Channel Videos Web Scrapping
  • Real-time Share Price scrapping and analysis

Data Analysis & Visualizations

Go through on a data-driven journey with these captivating exploratory data analysis and visualization projects.

  • Zomato Data Analysis Using Python
  • IPL Data Analysis
  • Airbnb Data Analysis
  • Global Covid-19 Data Analysis and Visualizations
  • Housing Price Analysis & Predictions
  • Market Basket Analysis
  • Titanic Dataset Analysis and Survival Predictions
  • Iris Flower Dataset Analysis and Predictions
  • Customer Churn Analysis
  • Car Price Prediction Analysis
  • Indian Election Data Analysis
  • HR Analytics to Track Employee Performance
  • Product Recommendation Analysis
  • Credit Card Approvals Analysis & Predictions
  • Uber Trips Data Analysis
  • iPhone Sales Analysis
  • Google Search Analysis
  • World Happiness Report Analysis & Visualization
  • Apple Smart Watch Data Analysis
  • Analyze International Debt Statistics

Dive into the world of machine learning with these real world data science practical projects.

  • Wine Quality Prediction
  • Credit Card Fraud Detection
  • Disease Prediction Using Machine Learning
  • Loan Approval Prediction using Machine Learning
  • Loan Eligibility prediction using Machine Learning Models in Python
  • Recommendation System in Python
  • ML | Heart Disease Prediction Using Logistic Regression
  • House Price Prediction using Machine Learning in Python
  • ML | Boston Housing Kaggle Challenge with Linear Regression
  • ML | Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
  • ML | Cancer cell classification using Scikit-learn
  • Stock Price Prediction using Machine Learning in Python
  • ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross-Validation
  • Box Office Revenue Prediction Using Linear Regression in ML
  • Online Payment Fraud Detection using Machine Learning in Python
  • Customer Segmentation using Unsupervised Machine Learning in Python
  • Bitcoin Price Prediction using Machine Learning in Python
  • Recognizing HandWritten Digits in Scikit Learn
  • Zillow Home Value (Zestimate) Prediction in ML
  • Calories Burnt Prediction using Machine Learning

Time Series & Forecasting

Data Sceince Projects on time series and forecasting-

  • Time Series Analysis with Stock Price Data
  • Weather Data Analysis
  • Time Series Analysis with Cryptocurrency Data
  • Climate Change Data Analysis
  • Anomaly Detection in Time Series Data
  • Sales Forecast Prediction – Python
  • Predictive Modeling for Sales or Demand Forecasting
  • Air Quality Data Analysis and Dynamic Visualizations
  • Gold Price Analysis and Forcasting Over Time
  • Food Price Forecasting
  • Time wise Unemployement Data Analysis
  • Dogecoin Price Prediction with Machine Learning

Dive into these Data Science projects on Deep Learning to see how smart computers can get!

  • Prediction of Wine type using Deep Learning
  • IPL Score Prediction Using Deep Learning
  • Handwritten Digit Recognition using Neural Network
  • Predict Fuel Efficiency Using Tensorflow in Python
  • Identifying handwritten digits using Logistic Regression in PyTorch

Explore fascinating Data Science projects with OpenCV, a cool tool for playing with images and videos. You can do fun tasks like recognizing faces , tracking objects , and even creating your own Snapchat-like filters . Let’s unleash the power of computer vision together!

  • OCR of Handwritten digits | OpenCV
  • Cartooning an Image using OpenCV – Python
  • Count number of Object using Python-OpenCV
  • Count number of Faces using Python – OpenCV
  • Text Detection and Extraction using OpenCV and OCR

Discover the magic of NLP (Natural Language Processing) projects , where computers learn to understand human language. Dive into exciting tasks like sentiment analysis, chatbots, and language translation. Join the adventure of teaching computers to speak our language through these exciting projects.

  • Detecting Spam Emails Using Tensorflow in Python
  • SMS Spam Detection using TensorFlow in Python
  • Flipkart Reviews Sentiment Analysis using Python
  • Fake News Detection using Machine Learning
  • Fake News Detection Model using TensorFlow in Python
  • Twitter Sentiment Analysis using Python
  • Facebook Sentiment Analysis using python
  • Hate Speech Detection using Deep Learning

In this journey through data science projects, we’ve explored a vast array of fascinating topics and applications. From uncovering insights in web scraping and exploratory data analysis to solving real-world problems with machine learning, deep learning, OpenCV, and NLP, we’ve witnessed the power of data-driven insights.

Whether it’s predicting wine quality or detecting fraud, analyzing sentiments or forecasting sales, each project showcases how data science transforms raw data into actionable knowledge. With these projects, we’ve unlocked the potential of technology to make smarter decisions, improve processes, and enrich our understanding of the world around us.

What projects can be done in data science?

Data science projects can include web scraping, exploratory data analysis, machine learning, deep learning, computer vision, natural language processing, and more.

Which project is good for data science?

One of the most basic yet popular data science project is customer segmentation . Product based or service based, all companies need to work such that they can capture maximum users. This makes customer segmentation an important project.

How do I choose a data science project?

Choose a data science project based on your interests, available data, relevance to your goals, and potential impact on solving real-world problems.

What are the 10 main components of a data science project?

The 10 main components of a data science project include problem definition, data collection, data cleaning, exploratory data analysis, feature engineering, model selection, model training, model evaluation, results interpretation, and communication.

Are ML projects good for resume?

ML projects are excellent additions to a resume, showcasing practical skills, problem-solving abilities, and the ability to derive insights from data.

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15 Data Science Projects that Will Land You a Job in 2023

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  • Published on November 18, 2022
  • by Mohit Pandey

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Getting into the dynamic field of data science requires you to catch up and build on the trends of the industry. Building your portfolio is the right direction for it and solving the existing problems that can orchestrate breakthroughs in the industry is the perfect path to take. Finding the right project that fits your knowledge, matches with requirements of the industry, and gives you real world practical experience is a decision-heavy task. 

We have compiled a  list of trending data science projects that you can explore to help refine your resume and land a job of your choice in 2023!

Sentiment Analysis

For natural language processing, this data science project involves determining whether the data inferred is positive, negative, or neutral. This can help social media platforms analyse posts and the emotions behind them, which can then be insightful for review information on public sites.

Machine learning involves a lot of processes that, if automated, can increase the efficiency of researchers and scientists. Scaling time-consuming tasks to run automatically can limit the time spent on machine learning tasks that are rather redundant.

Detection of Fake News

Identification and classification of fake news is the need of the hour. Using Python, developers can build a machine learning model that judges and predicts misleading journalism on digital platforms. Using classifiers like ‘PassiveAggressive’ or ‘Inverse Document Frequency’, this data science project can move ahead in the right direction.

Read : Top 10 Indian Government Datasets

Movie Recommender

The recommendation systems of OTT platforms work decently well even in their current state. It works on two different systems—one of collaborative filtering and another, content based filtering. The collaboration of both these into one single recommendation based on browsing habits of others with the similar taste in movies is an ideal project to take on.

Automated Data Cleaning

The accuracy and efficiency of a machine learning model is dependent on the data that it is trained on. An algorithm that can detect and correct flaws in the data without the need for manual intensive labour can help scientists and researchers focus on the higher impact of machine learning models.

Interactive Data Visualisation

Graphs and charts are the best way to display information about a topic. Creating interactive elements in data visualisation can attract more attention to the topic and result in effective interpretation of the data. Businesses are actively regarding interactive data visualisation as critical for decision making.

Recognition of Speech Emotion

Similar to sentiment analysis in text, identifying emotion in speech can help in customisation of the needs of individuals. An intermediate level project, it leverages several algorithms into a single project and can solve a lot of marketing and research problems in speech recognition.

Customer Segmentation

The most popular and trendy data science projects related to digital marketing, customer segmentation deals with clustering methods to identify the customer choices and delivering products based on the habits, interests areas, and more—including the data of annual income of the customers.

Read : The Ultimate Guide to Cracking Data Science Interview

Forest Fire Prediction

Predicting forest fires beforehand can help tackle disasters and prevent significant damage to the ecosystem. Similar to customer segmentation, this project can also leverage k-means clustering to identify hotspots for fire using the meteorological data such as the seasons when fires are more prone and frequent to occur.

Credit Card Fraud Detection Project

An advanced level project, detecting credit card fraud using datasets of card transactions and implementing them on algorithms like decision tree, logistic regression, artificial neural networks, and gradient boosting classifier will help you fit different algorithms in a single model and upskill for better opportunities in the industry.

Stock Market Prediction

Though stock prices are extremely volatile and difficult to predict, there are various organisations and researchers actively trying to build a model that can predict the rise and fall of stocks in the market. A machine learning model based on the stock market data along with natural language processing can be an excellent, albeit risky, project to build.

Sound Classification

Speech separation has always been a difficult problem to solve in machine learning. Improving and building on speech recognition systems using natural language processing is the need of the hour in the AI industry and efforts in this direction can propel your professional career towards great success.

Road Traffic Prediction

Along with detecting road lanes and lines, predicting the traffic-clustered areas of a city is a major task for furthering research in automation of vehicles. Similar to classification and detection of hotspots of fire prone areas, using the datasets of streets, accidents, and traffic signals, a machine learning model can definitely map areas chronically plagued with heavy traffic.

Crime Analysis

There are several failed machine learning models that were used either to predict crimes or within the criminal justice system. Building a reliable model that can deliver accurate crime predictions and analysis can help the government, police, and judicial system in their operations, and make your resume stand out among industry peers.

Store Sales Prediction

Based on the past trends of stores and the interested customers in the area, predicting the future sales of the store can help in action plans for the right products to be sold to the right consumers. This project can be used globally for better management and overall planning of the business.

Read : 9 Platforms for Building a Strong Data Science Portfolio

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Data Scientist Resume Examples For 2024 (20+ Skills & Templates)

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Looking to score a job as a Data Scientist?

You're going to need an awesome resume. This guide is your one-stop-shop for writing a job-winning Data Scientist resume using our proven strategies, skills, templates, and examples.

All of the content in this guide is based on data from coaching thousands of job seekers (just like you!) who went on to land offers at the world's best companies.

If you want to maximize your chances of landing that Data Scientist role, I recommend reading this piece from top to bottom. But if you're just looking for something specific, here's what's included in this guide:

  • What To Know About Writing A Job-Winning Data Scientist Resume
  • The Best Skills To Include On A Data Scientist Resume

How To Write A Job-Winning Data Scientist Resume Summary

How to write offer-winning data scientist resume bullets.

  • 3 Data Scientist Resume Examples

The 8 Best Data Scientist Resume Templates

Here's the step-by-step breakdown:

Data Scientist Resume Overview: What To Know To Write A Resume That Wins More Job Offers

What do companies look for when they're hiring a Data Scientist?

Companies look for candidates with strong technical skills in programming languages like Python or R and experience with data manipulation, statistical analysis, and machine learning models. Companies are also looking for data scientists with problem-solving skills who can obtain actionable insights from complete datasets.

Your resume should show the company that your personality and your experience encompass all these things.

Additionally, there are a few best practices you want to follow to write a job-winning Data Scientist resume:

  • Tailor your resume to the job description you are applying for: Tailor your resume for each application, aligning your skills with the specific requirements of each job description.
  • Detail previous experiences: Provide detailed descriptions of your roles, emphasizing hard and soft skills related to the job description.
  • Bring in your key achievements: Showcase measurable achievements in previous roles and share your best work.
  • Highlight your skills:   Highlight your skills in Sales, Marketing, Communication, Customer Experience, and Management.
  • Make it visually appealing: Use a professional and clean layout with bullet points for easy readability. Also, ensure formatting and font consistency throughout the resume and limit it to one or two pages.
  • Use keywords: Incorporate industry-specific keywords from the job description to pass through applicant tracking systems (ATS) and increase your chances of being noticed by hiring managers.
  • Proofread your resume: Thoroughly proofread your resume to eliminate errors (I recommend Hemingway App and Grammarly ). Consider seeking feedback from peers or mentors to ensure clarity and effectiveness!

Let's dive deeper into each of these so you have the exact blueprint you need to see success.

The Best Data Scientist Skills To Include On Your Resume

Keywords are one of the most important factors in your resume. They show employers that your skills align with the role and they also help format your resume for Applicant Tracking Systems (ATS).

If you're not familiar with ATS systems, they are pieces of software used by employers to manage job applications. They scan resumes for keywords and qualifications and make it easier for employers to filter and search for candidates whose qualifications match the role.

If you want to win more interviews and job offers, you need to have a keyword-optimized resume. There are two ways to find the right keywords:

1. Leverage The 20 Best Data Scientist Keywords

The first is to leverage our list of the best keywords and skills for a Data Scientist resume.

These keywords were selected from an analysis of real Data Scientist job descriptions sourced from actual job boards. Here they are:

  • Data Science
  • Communication
  • Machine Learning
  • Engineering
  • Cross-Functional
  • Organization
  • Collaboration
  • Descision Making

2. Use ResyMatch.io To Find The Best Keywords That Are Specific To Your Resume And Target Role

The second method is the one I recommend because it's personalized to your specific resume and target job.

This process lets you find the exact keywords that your resume is missing when compared to the individual role you're applying for.

Data Scientist Hard Skills

Here's how it works:

  • Open a copy of your updated Data Scientist resume
  • Open a copy of your target Data Scientist job description
  • In the widget below, paste your resume on the left, paste the job description on the right, and hit scan!

ResyMatch is going to scan your resume and compare it to the target job description. It's going to show you the exact keywords and skills you're missing as well as share other feedback you can use to improve your resume.

If you're ready to get started, use the widget below to run your first scan and get your free resume score:

data science projects to put on resume

Copy/paste or upload your resume here:

Click here to paste text

Upload a PDF, Word Doc, or TXT File

Paste the job post's details here:

Scan to compare and score your resume vs the job's description.

Scanning...

And if you're a visual learner, here's a video walking through the entire process so you can follow along:

Employers spend an average of six seconds reading your resume.

If you want to win more interviews and offers, you need to make that time count. That starts with hitting the reader with the exact information they're looking for right at the top of your resume.

Unfortunately, traditional resume advice like Summaries and Objectives don't accomplish that goal. If you want to win in today's market, you need a modern approach. I like to use something I can a “Highlight Reel,” here's how it works.

Highlight Reels: A Proven Way To Start Your Resume And Win More Jobs

The Highlight Reel is exactly what it sounds like.

It's a section at the top of your resume that allows you to pick and choose the best and most relevant experience to feature right at the top of your resume.

It's essentially a highlight reel of your career as it relates to this specific role! I like to think about it as the SportsCenter Top 10 of your resume.

The Highlight Reel resume summary consists of 4 parts:

  • A relevant section title that ties your experience to the role
  • An introductory bullet that summarizes your experience and high-level value
  • A few supporting “Case Study” bullets that illustrate specific results, projects, and relevant experience
  • A closing “Extracurricular” bullet to round out your candidacy

For example, if we were writing a Highlight Reel for a Data Scientist role, it might look like this:

Data Scientist Resume Summary Example #1 (New)

The first bullet includes the candidate's years of experience in the role and wraps up with a value-driven pitch about how they've helped companies in the past.

The next two bullets are “Case Studies” of specific results they drove at their company. The last bullet wraps up with extracurricular information.

This candidate has provided all of the info any employer would want to see right at the very top of their resume! The best part is that they can customize this section for each and every role they apply for to maximize the relevance of their experience.

Here's one more example of a Data Scientist Highlight Reel:

Data Scientist Resume Summary Example #2

The content of this example showcases a candidate transitioning from sales to data science, leveraging their experience with sales and bringing in measurable results in each bullet point. Then, they wrap up with a high-value extracurricular activity that's related to their target position.

If you want more details on writing a killer Highlight Reel, check out my full guide on Highlight Reels here.

Bullets make up the majority of the content in your resume. If you want to win, you need to know how to write bullets that are compelling and value-driven.

Unfortunately, way too many job seekers aren't good at this. They use fluffy, buzzword-fill language and they only talk about the actions that they took rather than the results and outcomes those actions created.

The Anatomy Of A Highly Effective Resume Bullet

If you apply this framework to each of the bullets on your resume, you're going to make them more compelling and your value is going to be crystal clear to the reader. For example, take a look at these resume bullets:

❌ Data Scientist with 5+ years of experience.

✅ Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%.

The second bullet makes the candidate's value  so much more clear, and it's a lot more fun to read! That's what we're going for here.

That said, it's one thing to look at the graphic above and try to apply the abstract concept of “35% hard skills” to your bullet. We wanted to make things easy, so we created a tool called ResyBullet.io that will actually give your resume bullet a score and show you how to improve it.

Using ResyBullet To Write Crazy Effective, Job-Winning Resume Bullets

ResyBullet takes our proprietary “resume bullet formula” and layers it into a tool that's super simple to use. Here's how it works:

  • Head over to ResyBullet.io
  • Copy a bullet from your resume and paste it into the tool, then hit “Analyze”
  • ResyBullet will score your resume bullet and show you exactly what you need to improve
  • You edit your bullet with the recommended changes and scan it again
  • Rinse and repeat until you get a score of 60+
  • Move on to the next bullet in your resume

Let's take a look at how this works for the two resume bullet examples I shared above:

First, we had, “Data Scientist with 5+ years of experience.” 

ResyBullet gave that a score of 35/100.  Not only is it too short, but it's missing relevant skills, compelling language, and measurable outcomes:

Example Of A Bad Data Scientist Resume Bullet

Now, let's take a look at our second bullet,  “Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%”.

ResyBullet gave that a 61 / 100. Much better! This bullet had more content focused on the experience in the Data Scientist role, while also highlighting measurable results:

Example Of A Good Data Scientist Resume Bullet

Now all you have to do is run each of your bullets through ResyBullet, make the suggested updates, and your resume is going to be jam-packed with eye-popping, value-driven content!

If you're ready, grab a bullet from your resume, paste it into the widget below, and hit scan to get your first resume bullet score and analysis:

Free Resume Bullet Analyzer

Learn to write crazy effective resume bullets that grab attention, illustrate value, and actually get results., copy and paste your resume bullet to begin analysis:, 3 data scientist resume examples for 2024.

Now let's take a look at all of these best practices in action. Here are three resume examples for different situations from people with different backgrounds:

Data Scientist Resume Example #1: A Traditional Background

Data Scientist Resume Example #1 - Traditional

Data Scientist Resume Example #2: A Non-Traditional Background

For our second Data Scientist Resume Example, we have a candidate who has a non-traditional background. In this case, they come from a background in sales but leverage experiences that have helped them transition to a Data Scientist role. Here's an example of what their resume might look like:

Data Scientist Resume Example #2 - Non-Traditional

Data Scientist Resume Example #3: Data Scientist New Grad

For our third Data Scientist Resume Example, we have a new graduate who's never worked for a company before but has worked on several self-initiated projects. Here's an example of what their resume might look like when applying for Data Scientist roles:

Data Scientist Resume Example #3 - New Grad

At this point, you know all of the basics you'll need to write a Data Scientist resume that wins you more interviews and offers. The only thing left is to take all of that information and apply it to a template that's going to help you get results.

We made that easy with our ResyBuild tool . It has 8 proven templates that were created with the help of recruiters and hiring managers at the world's best companies. These templates also bake in thousands of data points we have from the job seekers in our audience who have used them to land job offers.

Just click any of the templates below to start building your resume using proven, recruiter-approved templates:

data science projects to put on resume

Free Job-Winning Resume Templates, Build Yours In No Time .

Choose a resume template below to get started:.

data science projects to put on resume

Key Takeaways To Wrap Up Your Job-Winning Data Scientist Resume

You made it! We packed a lot of information into this post so I wanted to distill the key points for you and lay out next steps so you know exactly where to from here.

Here are the 5 steps for writing a job-winning Data Scientist resume:

  • Start with a proven resume template from ResyBuild.io
  • Use ResyMatch.io to find the right keywords and optimize your resume for each role you apply to
  • Open your resume with a Highlight Reel to immediately grab your target employer's attention
  • Use ResyBullet.io to craft compelling, value-driven bullets that pop off the page
  • Compare the draft of your resume to the examples on this page to make sure you're on the right path
  • Use a tool like HemingwayApp or Grammarly to proofread your resume before you submit it

If you follow those steps, you're going to be well on your way to landing more Data Scientist interviews and job offers.

Now that your resume is taken care of, check out my guide on how to get a job anywhere without applying online!

data science projects to put on resume

Paula Martins

Paula is Cultivated Culture's amazing Editor and Content Manager. Her background is in journalism and she's transitioned from roles in education, to tech, to finance, and more. She blends her journalism background with her job search experience to share advice aimed at helping people like you land jobs they love without applying online.

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5 Data Engineering Projects To Add To Your Resume

data engineering projects

Photo by  Green Chameleon  on  Unsplash

All signs point towards an auspicious future for data engineering..

Dice’s 2020 tech jobs report cites Data Engineering as the fastest growing job in 2020. Increasing by a staggering 50%,  while Data Science roles only increased by 10% . You can rest assured that the influx of data engineering will not regress anytime soon. To bolster this supposition, the International Data Group (IDG) predicts that the five year compound growth rate (CAGR) of data utilization from 2021 to 2024 will outweigh the total data creation spanning the entirety of the last thirty years. Yes, you heard that correctly, thirty years, dating back far before the origins of both FaceBook, YouTube and Amazon.

data engineering project ideas

If you are still not sold on the prospect of data engineering, let’s look into earning potential.

As of May 9th, 2021, with over eight thousand salaries reported, Indeed indicates that data engineers make $10,000 more per year than data scientists. Additionally, the benefits of data engineering do not stop at pay alone, a study from The New Stack indicates that there is less competition for data engineering roles than other tech positions.

The New Stack found that for LinkedIn and Indeed job posts, for every open data science position there were 4.76 viable applicants, while data engineering roles experience only 2.53 suitable competitors per job opening. Nearly, doubling the chances of obtaining a data engineering role for applicable candidates.

We have established that data engineering is a well-paying position, in one of the fastest-growing tech fields, with relatively low competition. What is not to love?

However, merely graduating from a relative field alone will not qualify you for a data engineering position.

You’ll need related real-world experience to fine-tune your hard skills. Concerning your future job search, one of the best ways to develop and convey these skills is through akin data engineering portfolio projects. In this article, we will review five potential project ideas with data sources. Before we cover the projects, you need to know the skills you should include in potential projects. For that, we will explore the most in-demand skillsets for data engineers.

What should you look for in a data engineering project?

When you look to build a  data engineering project  there are a few key areas you should focus on.

  • Multiple Types Of Data Sources(APIs, Webpages, CSVs, JSON, etc)
  • Data Ingestion
  • Storing Data
  • Data Visualization (So you have something to show for your efforts).
  • Use Of Multiple Tools (Even if some tools may not be the perfect solution, why not experiment with Kinesis or Spark to become familiar with them?)

Each of these areas will help you as a data engineer improve your skills and understand the data pipeline as a whole. In particular, creating some sort of end visual, especially if it involves creating a basic website to host it can be a fun way to show your projects off.

But enough talk, let’s dig into some ideas for your  data engineering projects .

Scrape Stock And Twitter Data Using Python, Kafka and Spark

With the expansion of cryptocurrency exchanges and the rise and fall of GameStop stock, stocks have become a hot issue, gaining substantial outsider interest.

If you have also developed a zeal for trading markets I would suggest developing a project similar to CashTag, a project that was developed by an engineer currently working at Reddit. The goal of this project was to develop a “Big data pipeline for user sentiment analysis on the US stock market”. In short, this project scrapes social media with the intent of predicting how people may feel about particular stocks in real-time. Below is a representation of the workflow used in this project.

data science projects to put on resume

This project is well documented and can be used as a base of inspiration for your project, which you can appropriate to accommodate your interest.

Scrape Real-Estate Properties With Python And Create A Dashboard With It

To engage with some new technologies, you should try a project like sspaeti’s 20 minute data engineering project. The goal of this project is to develop a tool that can be used to optimize your choice of house/rental property.

This project collects data using web scraping tools such as Beautiful Soup and Scrapy. Creating Python scripts that interact with HTML is something that you should be exposed to as a data engineer and web scraping is a great way to learn. Interestingly this project covers both Delta Lake and Kubernetes, which are hot topics at the moment.

Lastly, no good data engineering project is complete without having a clean UI to show your work. For example, this project uses Superset for your UI. Overall, the sheer variety of tools used in this project make it perfect for a portfolio.

Focus On Analytics With StackOverflow Data

Project Idea 3

What if you could analyze all or at least some of the public Github repos. What questions would you ask?

Felipe Hoffa  has already done some work on this type of project where he  analyzed terabytes of data over several articles  from the Google BigQuery data collection.

But with so much data, there is a lot of opportunities to work on some form of analytical project. Felipe, for example, analyzed concepts like:

  • Tabs vs Spaces?
  • Which programming languages do developers commit to during the weekend?
  • Analyzing GitHub Repos for comments and questions

There are so many different angles you could take on this project and it provides, you, the data engineer a lot of creativity in how you think about data.

You can analyze the source code of 2.8 million projects.

Maybe you can write an article like  What StackOverflow Code Snippets Can We Find In GitHub?

In addition, this project idea should also point out that there are plenty of interesting data sets you can use out there that exist on platforms like  GCP  and  AWS . So if you don’t feel like scraping data from an API, you can always work on your analytical chops on the hundreds of data sets these two cloud providers to offer.

Instead Of Stocks Predict Politics And Financial Events With PredictIt

Extending outside of stock prediction, PredictIt makes market data available via an API. PredictIt actually has a pretty amazing API. Predictit is a New Zealand-based online prediction market that offers exchanges for global political and financial events. You may be familiar with the reported betting odds of the last election cycle, when these numbers are reported they are citing markets similar to Predictit.

Using their live API data you can cross reference spikes with news potentially, tying in scraped data from social media. Like the CashTag project previously discussed. You could find a way to tie online political chatter to a dollar value.

Of course, why stop there? Why not try to create a data storage system using something like BigQuery and add in other data like tweets, news, and so on?

Then spend time normalizing that data and trying to create tables that represent connections between all these disparate data sources.

Now that would be a fun and challenging  data engineering  project.

Scraping Inflation Data And Developing A Model With Data From CommonCrawl

Another interesting project was conducted by Dr. Usama Hussain, where he measured the rate of inflation by tracking the change of price of goods and services online. Considering that the BBC reports that the United States has seen the largest inflation rate since 2008, this is an important topic.

In this project, the author used petabytes of web page data contained in the  Common Crawl .

I also think this is another great example of putting together and displaying a data engineering project. One of the challenges I often reference is how hard it can be to show off your data engineering work.

But Dr. Hussain’s project is documented in a way that shows off what work was done and the skills that he has, without having to dig into all of the code.

Dr. Hussain outlines the data pipeline below.

data science projects to put on resume

When it comes to selecting a project, the best project is one that strikes a balance between the interest of industry and personal interest. Whether you like it or not, personal interest is conveyed through the topic you choose, so it is important to find a project that you like. Stocks, real estate, politics, or some other niche category, what project will you take on?

Thanks for reading! If you want to read more about data consulting, big data, and data science, then click below.

Building Your First Data Pipeline: How To Build A Task In Luigi Part 1

Greylock VC and 5 Data Analytics Companies It Invests In

8 Data Engineering Best Practices

How To Improve Your Data-Driven Strategy

Mistakes That Are Ruining Your Data-Driven Strategy

5 Great Libraries To Manage Big Data With Python

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15 R Projects for Beginners (with Source Code)

R programming projects are essential for gaining practical data science experience. They provide the hands-on practice that bridges the gap between learning the required skills and deomonstrating you meet real-world job requirements. This process is particularly valuable when applying for jobs, as it addresses the common challenge of not having any experience when you're applying for your first data job .

A properly diversified portfolio of R projects will demonstrate your proficiency in:

  • Data manipulation
  • Data visualization
  • Advanced statistical analysis

These skills are fundamental to making informed business decisions―so being able to demonstrate that you have them makes you a valuable asset to potential employers.

In this post, we'll explore 15 practical R project ideas. Each project is designed to highlight critical data science capabilities that will enhance your job prospects. Whether you're a student aiming to launch your career or a professional seeking advancement, these projects on R will show your ability to handle real-world data challenges effectively.

Two individuals collaborating over an R project, highlighting the importance of practical experience

But first, to ensure you're developing in-demand R skills , we'll explain how to build your portfolio of projects on R by selecting the right ones and go over some of the common challenges you might face along the way. After we look at the 15 R project ideas in detail, we'll discuss how you can prepare for an R programming job.

Choosing the right R projects for your portfolio

Looking to improve your chances of landing a data science job? The R project ideas you select for your portfolio can make a big difference. A well-chosen set of projects on R shows off your skills and proves you can tackle real-world problems. Here's how to select R projects that help you grow, match your interests, and impress potential employers.

Find the sweet spot: Your skills, interests, and market demand

The best projects combine what you enjoy, what you're good at, and what employers want. This balance keeps you motivated and makes you more appealing to hiring managers. For example, if you love sports, you might create a project that uses R to predict game outcomes. This type of project lets you practice working with data and creating visualizations—skills that are valuable in many industries.

How to pick your R projects: A step-by-step approach

  • Know your strengths (and weaknesses): Assess your R programming skills. What are you comfortable with? Where do you need practice? Knowing the answers these questions will help you choose projects that challenge you appropriately.
  • Explore different tools and techniques: Pick projects that use various R packages and data types. This shows your versatility as a data scientist.
  • Focus on solving problems: ChR project ideasoose projects with clear goals, like predicting customer behavior or analyzing social media trends. These projects are engaging and show employers you can deliver results.
  • Seek feedback: Ask others to review your code and approach. Their input can help you improve your skills and projects.

Common challenges (and how to overcome them)

Many learners struggle with choosing projects on R that are too complex or aren't able to manage their time effectively. To avoid these issues:

  • Start small : Begin with manageable projects that match your current skill level.
  • Use available resources : When you get stuck, look for help in online tutorials or community forums .

Keep improving: The power of iteration

Don't stop after your first attempt. Reworking and refining your R projects based on feedback is key. This process of continuous improvement enhances the quality of your work and shows potential employers your commitment to excellence. It also helps prepare you for the workplace where iterating on your work is common.

Wrapping up

Carefully selecting your R project ideas can significantly improve your skills and how you present them to potential employers. As you review the list of 15 R project ideas below, use these tips to choose projects that will strengthen your portfolio and align with your career goals.

Getting started with R programming projects

Hands-on projects are key to developing practical R programming skills. They'll boost your understanding of the language and prepare you for real-world data tasks. Here's how to get started:

Common tools and packages

First, familiarize yourself with these R tools and packages:

  • RStudio: An IDE that simplifies code writing, debugging, and visualization .
  • dplyr : Streamlines data manipulation tasks .
  • ggplot2 : Creates complex visualizations easily .
  • data.table : Processes large datasets efficiently .

These tools will streamline your project workflow. For more insights, explore this guide on impactful R packages .

Setting up your project on R

Follow these steps to start your R programming project:

  • Install R and RStudio: These are your foundational tools .
  • Create a new project in RStudio: This keeps your files organized.
  • Learn the RStudio environment: Understand each part of the IDE to get the most out of it .
  • Import necessary packages: Load libraries like tidyverse or shiny as needed.

Overcoming common challenges

As a beginner, you might face some hurdles. Here are some strategies to help:

  • Keep your code organized and use Git for version control.
  • Start small to build confidence before tackling complex projects.
  • Use community forums and official documentation when you need help.

15 R Project Ideas with Source Code

The beauty of the following R projects lies in their diverse range of scenarios. You'll start by investigating COVID-19 virus trends and soon find yourself analyzing forest fire data. This variety ensures that you can apply your R programming skills to uncover valuable insights in different contexts. Although most of these R projects are suitable for beginners, the more advanced ones towards the end of the list may require additional effort and expertise to complete.

Here's what we'll cover:

Beginner R Projects

  • Investigating COVID-19 Virus Trends
  • Creating An Efficient Data Analysis Workflow
  • Creating An Efficient Data Analysis Workflow, Part 2
  • Analyzing Forest Fire Data
  • NYC Schools Perceptions
  • Analyzing Movie Ratings

Intermediate R Projects

  • New York Solar Resource Data
  • Investigating Fandango Movie Ratings
  • Finding the Best Markets to Advertise In
  • Mobile App for Lottery Addiction
  • Building a Spam Filter with Naive Bayes
  • Winning Jeopardy

Advanced R Projects

  • Predicting Condominium Sale Prices
  • Predicting Car Prices
  • Creating a Project Portfolio

In the sections that follow, we'll provide detailed walkthroughs for each project. You'll find step-by-step instructions and expected outcomes to guide you through the process. Let's get started with building your portfolio of projects on R!

1. Investigating COVID-19 Virus Trends

Difficulty Level: Beginner

In this beginner-level R project, you'll step into the role of a data analyst exploring the global COVID-19 pandemic using real-world data. Leveraging R and the powerful dplyr library, you'll manipulate, filter, and aggregate a comprehensive dataset containing information on COVID-19 cases, tests, and hospitalizations across different countries. By applying data wrangling techniques such as grouping and summarizing, you'll uncover which countries have the highest rates of positive COVID-19 tests relative to their testing numbers. This hands-on project will not only strengthen your R programming skills and analytical thinking but also provide valuable experience in deriving actionable insights from real-world health data – a crucial skill in today's data-driven healthcare landscape.

Tools and Technologies

Prerequisites.

To successfully complete this project, you should be comfortable with data structures in R such as:

  • Creating and working with vectors, matrices, and lists in R
  • Indexing data structures to extract elements for analysis
  • Applying functions to data structures to perform calculations
  • Manipulating and analyzing data using dataframes

Step-by-Step Instructions

  • Load and explore the COVID-19 dataset using readr and tibble
  • Filter and select relevant data using dplyr functions
  • Aggregate data by country and calculate summary statistics
  • Identify top countries by testing numbers and positive case ratios
  • Create vectors and matrices to store key findings
  • Compile results into a comprehensive list structure

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience, including:

  • Analyzing a real-world COVID-19 dataset using R and dplyr
  • Applying data manipulation techniques to filter and aggregate data
  • Identifying trends and insights from data using grouping and summarizing
  • Creating and manipulating different R data structures (vectors, matrices, lists)
  • Interpreting results to answer specific questions about COVID-19 testing and positive rates

Relevant Links and Resources

  • R Project Example Solution
  • Original COVID19 Worldwide Testing dataset on Kaggle

Additional Resources

  • WHO Coronavirus (COVID-19) Dashboard

2. Creating An Efficient Data Analysis Workflow

In this hands-on, beginner-level project with R, you'll step into the role of a data analyst for a company selling programming books. Using R and RStudio, you'll analyze their sales data to determine which titles are most profitable. By applying key R programming concepts like control flow, loops, and functions, you'll develop an efficient data analysis workflow. This project provides valuable practice in data cleaning, transformation, and analysis, culminating in a structured report of your findings and recommendations.

To successfully complete this project, you should be comfortable with control flow, iteration, and functions in R including:

  • Implementing control flow using if-else statements
  • Employing for loops and while loops for iteration
  • Writing custom functions to modularize code
  • Combining control flow, loops, and functions in R
  • Load and explore the book sales dataset using tidyverse
  • Clean the data by handling missing values and inconsistent labels
  • Transform the review data into numerical format
  • Analyze the cleaned data to identify top-performing titles
  • Summarize findings and provide data-driven recommendations
  • Applying R programming concepts to real-world data analysis
  • Developing an efficient, reproducible data analysis workflow
  • Cleaning and preparing messy data for analysis using tidyverse
  • Analyzing sales data to derive actionable business insights
  • Communicating findings and recommendations to stakeholders
  • Getting Started with R and RStudio - Dataquest Blog

In this beginner-level R project, you'll step into the role of a data analyst at a book company tasked with evaluating the impact of a new program launched on July 1, 2019 to encourage customers to buy more books. Using R and powerful packages like dplyr, stringr, and lubridate, you'll clean and analyze the company's 2019 sales data to determine if the program successfully boosted book purchases and improved review quality. You'll handle missing data, process text reviews, and compare key metrics before and after the program launch. This project offers hands-on experience in applying data manipulation techniques to real-world business data, strengthening your skills in efficient data analysis and deriving actionable insights.

  • tidyverse (including dplyr)

To successfully complete this project, you should be comfortable with specialized data processing techniques in R , including:

  • Manipulating strings using stringr functions
  • Working with dates and times using lubridate
  • Applying the map function to vectorize custom functions
  • Understanding and employing regular expressions for pattern matching
  • Load and explore the book company's 2019 sales data
  • Clean the data by handling missing values and inconsistencies
  • Process text reviews to determine positive/negative sentiment
  • Compare key sales metrics before and after the program launch date
  • Analyze differences in sales between customer segments
  • Evaluate changes in review sentiment and summarize findings
  • Cleaning and preparing a real-world business dataset for analysis using R
  • Applying powerful R packages to manipulate and process data efficiently
  • Analyzing sales data to quantify the impact of a new business initiative
  • Translating data analysis findings into meaningful business insights
  • Project Dataset

4. Analyzing Forest Fire Data

In this beginner-level data analysis project in R, you'll analyze a dataset on forest fires in Portugal to uncover patterns in fire occurrence and severity. Using R and powerful data visualization techniques, you'll explore factors such as temperature, humidity, and wind speed to understand their relationship with fire spread. You'll create engaging visualizations, including bar charts, box plots, and scatter plots, to reveal trends over time and across different variables. By completing this project, you'll gain valuable insights into the ecological impact of forest fires while strengthening your skills in data manipulation, exploratory data analysis, and creating meaningful visualizations using R and ggplot2.

  • tidyverse (including ggplot2)

To successfully complete this project, you should be comfortable with data visualization techniques in R and have experience with:

  • Working with variables, data types, and data structures in R
  • Importing and manipulating data using R data frames
  • Creating basic plots using ggplot2 (e.g., bar charts, scatter plots)
  • Transforming and preparing data for visualization
  • Load and explore the forest fires dataset using R and tidyverse
  • Process the data, converting relevant columns to appropriate data types (e.g., factors for month and day)
  • Create bar charts to analyze fire occurrence patterns by month and day of the week
  • Use box plots to explore relationships between environmental factors and fire severity
  • Implement scatter plots to investigate potential outliers and their impact on the analysis
  • Summarize findings and discuss implications for forest fire prevention strategies
  • Cleaning and preparing real-world ecological data for analysis using R
  • Creating various types of plots (bar charts, box plots, scatter plots) using ggplot2
  • Interpreting visualizations to identify trends in forest fire occurrence and severity
  • Handling outliers and understanding their impact on data analysis and visualization
  • Communicating data-driven insights for environmental decision-making
  • UCI Machine Learning Repository: Forest Fires Dataset

5. NYC Schools Perceptions

In this beginner-level R project, you'll explore real-world survey data on school quality perceptions in New York City. Using R and various data manipulation packages, you'll clean, reshape, and visualize responses from students, parents, and teachers to uncover insights about school performance. You'll work with a large, complex dataset to build valuable data wrangling and exploration skills while creating an impactful analysis of NYC school quality perceptions across different stakeholder groups.

  • R Notebooks
  • tidyverse (dplyr, tidyr, ggplot2)

To successfully complete this project, you should be comfortable with data cleaning techniques in R including:

  • Manipulating DataFrames using dplyr
  • Joining and combining relational data
  • Handling missing data through various techniques
  • Reshaping data between wide and long formats using tidyr
  • Creating visualizations with ggplot2
  • Load and clean the NYC school survey datasets
  • Join survey data with school performance data
  • Create a correlation matrix to identify relationships between variables
  • Visualize strong correlations using scatter plots
  • Reshape the data to compare perceptions across stakeholder groups
  • Analyze and visualize differences in perceptions using box plots
  • Cleaning and wrangling complex, real-world datasets using tidyverse tools
  • Joining multiple datasets to create a comprehensive analysis
  • Identifying correlations and visualizing relationships in data
  • Reshaping data to facilitate comparisons across different groups
  • Creating informative visualizations to communicate insights about school quality perceptions
  • Interpreting results to draw meaningful conclusions about NYC schools
  • NYC School Survey Data on NYC Open Data

6. Analyzing Movie Ratings

In this beginner-level project with R, you'll analyze movie ratings data from IMDb using web scraping techniques in R. You'll extract information such as titles, release years, runtimes, genres, ratings, and vote counts for the top 30 movies released between March and July 2020. Using packages like rvest and dplyr, you'll practice loading web pages, identifying CSS selectors, and extracting specific data elements. You'll also gain experience in data cleaning by handling missing values. Finally, you'll use ggplot2 to visualize the relationship between user ratings and number of votes, uncovering trends in movie popularity and reception. This project offers hands-on experience in web scraping, data manipulation, and visualization using R, skills that are highly valuable in real-world data analysis scenarios.

To successfully complete this project, you should be familiar with web scraping techniques in R and have experience with:

  • Understanding HTML structure and using CSS selectors to locate specific elements
  • Using the rvest package to extract data from web pages
  • Basic data manipulation and cleaning using dplyr and stringr
  • Working with vectors and data frames in R
  • Load the IMDb web page and extract movie titles and release years
  • Extract additional movie features such as runtimes and genres
  • Scrape user ratings, metascores, and vote counts for each movie
  • Clean the extracted data and handle missing values
  • Create a data frame combi ning all extracted information
  • Visualize the relationship between user ratings and vote counts using ggplot2
  • Implementing web scraping techniques to extract structured data from IMDb
  • Cleaning and preprocessing scraped data for analysis
  • Creating a comprehensive dataset of movie information from multiple web elements
  • Visualizing relationships between movie ratings and popularity
  • Applying R programming skills to solve real-world data extraction and analysis problems
  • IMDb Top 30 Movies (March-July 2020)

7. New York Solar Resource Data

Difficulty Level: Intermediate

In this beginner-friendly R project, you'll step into the role of a data analyst tasked with extracting solar resource data for New York City using the Data Gov API. Using R, you'll apply your skills in API querying, JSON parsing, and data structure manipulation to retrieve the data and convert it into a format suitable for analysis. This project provides hands-on experience in working with real-world data from web APIs, a crucial skill for data scientists working with diverse data sources.

To successfully complete this project, you should be comfortable with working with APIs in R and have experience with:

  • Making API requests using the httr package
  • Parsing JSON responses with jsonlite
  • Manipulating data frames using dplyr
  • Creating basic visualizations with ggplot2
  • Working with complex list structures in R
  • Set up the API request parameters and make a GET request to the NREL API
  • Parse the JSON response and extract relevant data into R objects
  • Convert the extracted data into a structured dataframe
  • Create a custom function to streamline the data extraction process
  • Visualize the solar resource data using ggplot2
  • Extracting data from web APIs using R and the httr package
  • Parsing and manipulating complex JSON data structures
  • Creating custom functions to automate data retrieval and processing
  • Visualizing time-series data related to solar resources
  • Applying data wrangling techniques to prepare API data for analysis
  • NREL Solar Resource Data API Documentation
  • Data.gov - Open Data Source

8. Investigating Fandango Movie Ratings

In this beginner-friendly project with R, you'll investigate potential bias in Fandango's movie rating system. A 2015 analysis revealed that Fandango's ratings were inflated. Your task is to compare movie ratings data from 2015 and 2016 to determine if Fandango's system changed after the bias was exposed. Using R and statistical analysis techniques, you'll explore rating distributions, calculate summary statistics, and visualize changes in rating patterns. This project provides hands-on experience with a real-world data integrity investigation, strengthening your skills in data manipulation, statistical analysis, and data visualization.

To successfully complete this project, you should be familiar with fundamental statistics concepts in R and have experience with:

  • Data manipulation using dplyr (filtering, selecting, mutating, summarizing)
  • Working with string data using stringr functions
  • Reshaping data with tidyr (gather, spread)
  • Calculating summary statistics (mean, median, mode)
  • Creating and customizing plots with ggplot2 (density plots, bar plots)
  • Interpreting frequency distributions and probability density functions
  • Basic hypothesis testing and statistical inference
  • Load and explore the 2015 and 2016 Fandango movie ratings datasets
  • Clean and preprocess the data, isolating relevant samples for analysis
  • Compare distribution shapes of 2015 and 2016 ratings using kernel density plots
  • Calculate and compare summary statistics for both years
  • Visualize changes in rating patterns using bar plots
  • Interpret results and draw conclusions about changes in Fandango's rating system
  • Conducting a comparative analysis of rating distributions using R
  • Applying statistical techniques to investigate potential bias in ratings
  • Creating informative visualizations to illustrate changes in rating patterns
  • Drawing and communicating data-driven conclusions about rating system integrity
  • Implementing end-to-end data analysis workflow in R, from data loading to insight generation
  • Original Fandango Ratings Dataset
  • Original FiveThirtyEight Article on Fandango Ratings

9. Finding the Best Markets to Advertise In

In this beginner-friendly R project, you'll step into the role of an analyst for an e-learning company offering programming courses. Your task is to analyze survey data from freeCodeCamp to determine the two best markets for advertising your company's products. Using R, you'll explore factors such as new coder locations, market densities, and willingness to pay for learning. By applying statistical concepts and data analysis techniques, you'll provide actionable insights to optimize your company's advertising strategy and drive growth.

To successfully complete this project, you should be comfortable with intermediate statistics concepts in R such as:

  • Summarizing distributions using measures of central tendency
  • Calculating variance and standard deviation
  • Standardizing values using z-scores
  • Locating specific values in distributions using z-scores
  • Load and explore the freeCodeCamp survey data
  • Analyze the locations and densities of new coders in different markets
  • Calculate and compare average monthly spending on learning across countries
  • Identify and handle outliers in the spending data
  • Determine the two best markets based on audience size and willingness to pay
  • Summarize findings and make recommendations for the advertising strategy
  • Applying statistical concepts to inform strategic business decisions
  • Using R to analyze real-world survey data and derive actionable insights
  • Handling outliers and cleaning data for more accurate analysis
  • Translating data analysis results into clear recommendations for stakeholders
  • Developing a data-driven approach to optimizing marketing strategies
  • The 2017 freeCodeCamp New Coder Survey Data
  • freeCodeCamp's New Coder Survey Results

10. Mobile App for Lottery Addiction

In this beginner-friendly data science project in R, you'll develop the logical core of a mobile app designed to help lottery addicts understand their chances of winning. As a data analyst at a medical institute, you'll use R programming, probability theory, and combinatorics to analyze historical data from the Canadian 6/49 lottery. You'll create functions to calculate various winning probabilities, check for previous winning combinations, and provide users with a realistic view of their odds. This project offers hands-on experience in applying statistical concepts to a real-world problem while building your R programming portfolio.

  • tidyverse package
  • sets package

To successfully complete this project, you should be comfortable with fundamental probability concepts in R such as:

  • Calculating theoretical and empirical probabilities
  • Applying basic probability rules
  • Working with permutations and combinations
  • Using R functions for complex probability calculations
  • Manipulating data with tidyverse packages
  • Implement core probability functions for lottery calculations
  • Calculate the probability of winning the jackpot with a single ticket
  • Analyze historical lottery data to check for previous winning combinations
  • Develop functions to calculate probabilities for multiple tickets and partial matches
  • Create user-friendly outputs to communicate lottery odds effectively
  • Applying probability and combinatorics concepts to a real-world scenario
  • Implementing complex probability calculations using R functions
  • Working with historical data to inform statistical analysis
  • Developing logical components for a mobile application
  • Communicating statistical concepts to a non-technical audience
  • 6/49 Lottery Dataset on Kaggle

11. Building a Spam Filter with Naive Bayes

In this beginner-friendly project with R, you'll build an SMS spam filter using the Naive Bayes algorithm. Working with a dataset of labeled SMS messages, you'll apply text preprocessing techniques, implement the Naive Bayes classifier from scratch, and evaluate its performance. This project offers hands-on experience in applying probability theory to a real-world text classification problem, providing valuable skills for aspiring data scientists in natural language processing and spam detection. You'll gain practical experience in data preparation, probability calculations, and implementing machine learning algorithms in R.

  • Naive Bayes algorithm

To successfully complete this project, you should be familiar with conditional probability concepts in R and have experience with:

  • Basic R programming and data manipulation using tidyverse
  • Understanding and applying conditional probability rules
  • Calculating probabilities based on prior knowledge using Bayes' theorem
  • Text preprocessing techniques in R
  • Load and preprocess the SMS dataset, creating training, cross-validation, and test sets
  • Clean the text data and build a vocabulary from the training set
  • Calculate probability parameters for the Naive Bayes classifier
  • Implement the Naive Bayes algorithm to classify new messages
  • Evaluate the model's performance and tune hyperparameters using cross-validation
  • Test the final model on the test set and interpret results
  • Implementing text preprocessing techniques for machine learning tasks
  • Building a Naive Bayes classifier from scratch in R
  • Applying probability calculations in a real-world text classification problem
  • Evaluating and optimizing machine learning model performance
  • Interpreting classification results in the context of spam detection
  • UCI Machine Learning Repository: SMS Spam Collection Dataset

12. Winning Jeopardy

In this beginner-friendly R project, you'll analyze a dataset of over 20,000 Jeopardy questions to uncover patterns that could give you an edge in the game. Using R and statistical techniques, you'll explore question categories, identify terms associated with high-value clues, and develop data-driven strategies to improve your odds of winning. You'll apply chi-squared tests and text analysis methods to determine which categories appear most frequently and which topics are associated with higher-value questions. This project will strengthen your skills in hypothesis testing, string manipulation, and deriving actionable insights from text data.

  • Chi-squared test

To successfully complete this project, you should be familiar with hypothesis testing in R and have experience with:

  • Performing chi-squared tests on categorical data
  • Manipulating strings and text data in R
  • Data cleaning and preprocessing techniques
  • Basic data visualization in R
  • Load and preprocess the Jeopardy dataset, cleaning text and converting data types
  • Normalize dates to make them more accessible for analysis
  • Analyze the frequency of question categories using chi-squared tests
  • Identify unique terms in questions and associate them with question values
  • Perform statistical tests to determine which terms are associated with high-value questions
  • Visualize and interpret the results to develop game strategies
  • Applying chi-squared tests to analyze categorical data in a real-world context
  • Implementing text preprocessing and analysis techniques in R
  • Interpreting statistical results to derive actionable insights
  • Developing data-driven strategies for game show success
  • Original Jeopardy Dataset

13. Predicting Condominium Sale Prices

Difficulty Level: Advanced

In this challenging project with R, you'll analyze New York City condominium sales data to predict prices based on property size. Using R and linear regression modeling techniques, you'll clean and explore the dataset, visualize relationships between variables, and build predictive models. You'll compare model performance across NYC's five boroughs (Manhattan, Brooklyn, Queens, The Bronx, and Staten Island), gaining valuable experience in real estate data analysis and statistical modeling. This project will strengthen your skills in data cleaning, exploratory analysis, and interpreting regression results in a practical business context.

  • Linear regression

To successfully complete this project, you should be familiar with linear regression modeling in R and have experience with:

  • Data manipulation and cleaning using tidyverse functions
  • Creating scatterplots and other visualizations with ggplot2
  • Fitting and interpreting linear regression models in R
  • Evaluating model performance using metrics like R-squared and RMSE
  • Basic understanding of real estate market dynamics
  • Load and clean the NYC condominium sales dataset
  • Perform exploratory data analysis, visualizing relationships between property size and sale price
  • Identify and handle outliers that may impact model performance
  • Build a linear regression model for all NYC boroughs combined
  • Create separate models for each borough and compare their performance
  • Interpret results and draw conclusions about price prediction across different areas of NYC
  • Cleaning and preparing real estate data for analysis in R
  • Visualizing and interpreting relationships between property features and prices
  • Building and comparing linear regression models across different market segments
  • Evaluating model performance and understanding limitations in real estate price prediction
  • Translating statistical results into actionable insights for real estate analysis
  • R-bloggers: A great resource for R programming tips and tutorials

14. Predicting Car Prices

In this challenging R project, you'll step into the role of a data scientist tasked with developing a model to predict car prices for a leading automotive company. Using a dataset of various car attributes such as make, fuel type, body style, and engine specifications, you'll apply the k-nearest neighbors algorithm in R to build an optimized prediction model. You'll go through the complete machine learning workflow - from data exploration and preprocessing to model evaluation and interpretation. This project will strengthen your skills in examining relationships between predictors, implementing cross-validation, performing hyperparameter optimization, and comparing different models to create an effective price prediction tool that could be used in real-world automotive market analysis.

  • caret package
  • k-nearest neighbors algorithm

To successfully complete this project, you should be comfortable with fundamental machine learning concepts in R such as:

  • Understanding the key steps in a typical machine learning workflow
  • Implementing k-nearest neighbors for regression tasks
  • Using the caret library for machine learning model training and evaluation in R
  • Evaluating model performance using error metrics (e.g., RMSE) and k-fold cross validation
  • Basic data manipulation and visualization using dplyr and ggplot2
  • Load and preprocess the car features and prices dataset, handling missing values and non-numerical columns
  • Explore relationships between variables using feature plots and identify potential predictors
  • Prepare training and test sets by splitting the data using createDataPartition
  • Implement k-nearest neighbors models using caret, experimenting with different values of k
  • Conduct 5-fold cross-validation and hyperparameter tuning to optimize model performance
  • Evaluate the final model on the test set, interpret results, and discuss potential improvements
  • Applying the end-to-end machine learning workflow in R to a real-world prediction problem
  • Implementing and optimizing k-nearest neighbors models for regression tasks using caret
  • Using resampling techniques like k-fold cross validation for robust model evaluation
  • Interpreting model performance metrics (e.g., RMSE) in the context of car price prediction
  • Gaining practical experience in feature selection, preprocessing, and hyperparameter tuning
  • Developing intuition for model selection and performance optimization in regression tasks
  • Original Automobile Dataset on UCI Machine Learning Repository

15. Creating a Project Portfolio

In this challenging project with R, you'll be tasked with creating an impressive interactive portfolio to showcase your R programming and data analysis skills to potential employers. Using Shiny, you'll compile your guided projects from Dataquest R courses into one cohesive portfolio app. You'll apply your Shiny skills to incorporate R Markdown files, customize your app's appearance, and deploy it for easy sharing. This project will strengthen your ability to create interactive web applications, integrate multiple data projects, and effectively present your work to enhance your job prospects in the data analysis field.

To successfully complete this project, you should be comfortable with building interactive web applications in Shiny and have experience with:

  • Understanding the structure and components of a Shiny app
  • Creating inputs and outputs in the Shiny user interface
  • Programming the server logic to connect inputs and outputs
  • Extending Shiny apps with additional features
  • Basic R Markdown usage for creating dynamic reports
  • Plan the structure and content of your portfolio app
  • Build the user interface with a navigation bar and project pages
  • Incorporate R Markdown files for individual project showcases
  • Develop server logic to handle user interactions and display content
  • Create a utility function to efficiently generate project pages
  • Design an engaging splash page and interactive resume section
  • Deploy your portfolio app to shinyapps.io for easy sharing
  • Building a comprehensive, interactive portfolio app using Shiny
  • Integrating multiple R projects and analyses into a cohesive presentation
  • Creating utility functions to streamline app development
  • Customizing Shiny app appearance and functionality for professional presentation
  • Deploying a Shiny app to a public hosting platform for easy access
  • Effectively showcasing your R programming and data analysis skills to potential employers
  • Resolved R Shiny app issue regarding images in the Dataquest Community
  • Non-Guided Project: Making an R Shiny App to track moths | Dataquest Community

How to Prepare for an R Programming Job

Looking to land your first R programming job? Let's walk through the key steps to prepare yourself for success in this field.

Understand Market Demands

Start by researching what employers want. Browse R programming job listings on popular job listing sites like the ones below. They'll give you a clear picture of the skills and qualifications currently in demand.

Once you have a good idea of the skills employers are looking for, take on projects that help you develop and demonstrate those in-demand skills.

Develop Essential Skills

For entry-level positions, focus on being able to demonstrate these skills:

  • Data manipulation (using packages like dplyr )
  • Data analysis and visualization (with tools like ggplot2 )
  • Basic statistical analysis
  • Fundamental machine learning concepts
  • Core programming principles

To build these skills:

  • Enroll in structured learning paths or bootcamps
  • Work on hands-on coding projects
  • Participate in coding competitions to enhance problem-solving skills

As you learn, you might find some concepts challenging. Don't get discouraged. Instead:

  • Practice coding regularly to improve your speed and accuracy
  • Seek feedback from peers or mentors to refine your code quality and problem-solving approach

Showcase Your Work

Create a portfolio that highlights your R projects. Include examples demonstrating your data analysis, visualization, and statistical computing skills. Consider using GitHub to host your work , ensuring each project is well-documented.

Prepare for the Job Hunt

Tailor your resume to emphasize relevant technical skills and project experiences. For interviews, be ready to discuss your projects in detail . Practice explaining how you've applied specific R functions and packages to solve real-world problems.

Remember, becoming job-ready in R programming is a journey that combines technical skill development, practical experience, and effective self-presentation. By following these steps and persistently honing your skills, you'll be well-equipped to pursue opportunities in the data science field using R.

Bottom line: R programming projects are essential for building real-world skills and advancing your data science career. Here's why they matter and how to get started:

  • Practical application : Projects help you apply theory to actual problems.
  • Career advancement : They showcase your abilities to potential employers.
  • Skill development : Start simple and gradually tackle more complex challenges.

If you're new to R, begin with basic projects focusing on data cleaning and visualization. This approach builds your confidence and expertise gradually. As you progress, adopt good coding practices. Clear, well-organized code is easier to read and maintain, especially when collaborating with others.

Consider exploring Dataquest's Data Analyst in R path . This program covers everything from basic concepts to advanced data techniques.

R projects do more than beef up your portfolio. They sharpen your problem-solving skills and prepare you for real data science challenges. Start with a project that interests you and matches your current skills. Then, step by step, move to more complex problems. Let your interest in data guide your learning journey.

Remember, every R project you complete brings you closer to your data science goals. So, pick a project and start coding!

More learning resources

Business analyst salary and job description 2024, 21 data science projects for beginners (with source code).

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Politics latest: Cameron resigns as Sunak names shadow cabinet; Labour MPs assemble for group photo

Sir Keir Starmer has assembled his Labour MPs for a huge group photo in Westminster after the party's landslide election win. Meanwhile, Rishi Sunak has reshuffled his top team as the Tories prepare for opposition.

Monday 8 July 2024 23:00, UK

  • General Election 2024

Please use Chrome browser for a more accessible video player

  • Sunak names shadow cabinet
  • Sam Coates analysis: Low-energy Tory reshuffle has one eye-catching move
  • Labour MPs assemble for huge group photo
  • Reeves outlines plan to boost housebuilding and reform planning
  • Ed Conway analysis: No big bang moment from chancellor, but hard reforms could one day deliver what UK's long struggled with
  • Live reporting by Faith Ridler

Election fallout

  • Starmer's challenges: Tackling exhausted NHS | Looming chaos abroad | Defence to dominate early days | Small boats plan? | Rift with scientists needs healing
  • Read more from Sky News: What to expect from Labour's first 100 days | Who's who in Starmer's inner circle | A look back at life when Labour last won power | Find our other must-read election features
  • Results in full: What happened in every constituency

Thanks for joining us for the start of the first full week of a Labour government in 14 years - and there's plenty more to come.

You can scroll through the page for today's updates, or check our 10pm post for a round-up of Monday's most significant news.

We'll be back at 6am with all the latest from Westminster.

The Conservative Party has announced a reshuffle, as former ministers and returning MPs make the transition into becoming the shadow cabinet.

Lord Cameron  has resigned from Rishi Sunak's frontbench, having been foreign secretary before Labour's victory in Thursday's election, and has now been replaced in the shadow role by his deputy Andrew Mitchell.

Also, despite clinging on to a seat in last week's vote,  Richard Holden  has quit as Tory party chairman, with Richard Fuller taking his place in the interim.

Writing in his resignation letter, Mr Holden said there needed to be a "thorough review into the general election campaign", but it would "best take place with a new set of eyes to help provide the clearest view".

You can read more from Sky News below:

 Former prime minister Lord Cameron has sought to explain why he resigned from Rishi Sunak's frontbench - and it has to do with his peerage.

As the ex-foreign secretary is not an MP, he is unable to enter the Commons and face-off with the new Foreign Secretary David Lammy.

Lord Cameron said that "clearly the Conservative Party in opposition will need to shadow the new Foreign Secretary from the Commons".

"So I told Rishi Sunak that I would step back."

However, the ex-PM said he is "delighted that the shadow foreign secretary role has gone to my good friend Andrew Mitchell".

We're coming to the end of the first day of the new Labour government's first full week in office.

Here's an easy catch-up on what you need to know tonight:

  • Rachel Reeves has delivered her first major speech as chancellor, pledging a "planning revolution" to boost housebuilding and allow new onshore wind projects to help deliver "sustained economic growth";
  • Our economics and data editor Ed Conway said while Ms Reeves' speech lacked any "big bang moment", the hard reforms she's promising could one day deliver what the UK's long struggled with.
  • Sir Keir Starmer has followed up his weekend visit to Scotland with trips to Northern Ireland and Wales, as he seeks to restore "mutual respect" between Westminster and the devolved governments;
  • Sir Keir's spokesperson  fielded questions from journalists today - they said the prime minister was keen for close relations with France whoever ends up in power there after the country's inconclusive elections.
  • A good relationship with France will be key to tackling the small boats crisis, which has continued today with the first migrant arrivals since the election ;
  • And the Conservatives have confirmed a shadow cabinet reshuffle tonight - with Lord Cameron out as shadow foreign secretary, and Richard Holden gone as Tory chairman.

That's all for now - but we'll have updates all day Tuesday.

By Mollie Malone, news correspondent

The government is expected to announce new plans to ease overcrowding in jails across England and Wales by the end of this week.

Sky News understands one of the core proposals being considered is a lowering of the automatic release point, from the 50% mark in their sentence, to 40 or 43%. 

At the moment prisoners serving standard determinate sentences - those with fixed end dates - are released at the halfway point. 

Once released they serve their sentence on licence. This change could mean thousands of additional inmates with earlier releases. 

Sexual, violent, and terror related offenders are excluded. 

'Immediate' problems - but 'no quick fix'

It comes as Justice Secretary Shabana Mahmood today met with representatives from across the prison service, at the beginning of her first full week in the role. 

Sky News understands Ms Mahmood was keen to emphasise her background as a barrister, experience in the sector, and the prime minister's former job as director of public prosecutions.

She expressed a desire to better embrace technology and AI to improve the efficiency of the service in the future. 

Ms Mahmood spoke of the "immediate" problems in prisons, though sources say little detail was provided, as the government continues to weigh up its options.

Sky News understands there are around 700 spaces left in male prisons across England and Wales. 

Home Secretary Yvette Cooper today admitted there is not going to be a "quick fix" to solve overcrowding in prisons, suggesting the government is "extremely concerned" by the situation they have inherited.

Sir Keir Starmer has welcomed his 411 MPs into parliament this afternoon, posing for a so-called family photograph days after a huge election win.

He says: "Fantastic to welcome all of our returning and new Labour MPs today. 

"The work of change begins now."

Sky News' deputy political editor Sam Coates and Politico's Jack Blanchard are back in your podcast feeds.

On this episode, they discuss how the prime minister is tackling his first full week and his government’s approach to home and foreign affairs.

And how will the Conservative Party choose a new leader?

All that more below:

👉  Tap here to follow Politics At Jack And Sam's wherever you get your podcasts  👈

Email Jack and Sam: [email protected]

With the change of government, the Conservatives' controversial Rwanda migration scheme has been scrapped.

Rishi Sunak had pledged to get planes carrying asylum seekers off the tarmac by the spring, and then shifted his target date to July should he have won the general election.

Sir Keir Starmer is now in the top job, and has stressed the Rwanda scheme was "dead" on day one of his government.

We've now heard from the Rwandan government, which has reiterated it "upheld its side of the agreement, including with regard to finances".

Kigali said it "takes note" of the UK government's intention to "terminate" the agreement.

It added: "This partnership was initiated by the government of the UK in order to address the crisis of irregular migration affecting the UK — a problem of the UK, not Rwanda.

"Rwanda has fully upheld its side of the agreement, including with regard to finances, and remains committed to finding solutions to the global migration crisis, including providing safety, dignity, and opportunity to refugees and migrants who come to our country."

Sir Keir Starmer heads to Washington for a NATO summit tomorrow, and we've just had confirmation his visit will include face-to-face talks with US President Joe Biden.

The White House said Mr Biden would host the new prime minister on Wednesday.

It comes after the pair spoke on the phone on Friday evening - a clip of which was released by Number 10.

We'll continue to have updates through the night right here.

And you can scroll down the page for all the latest from the Politics Hub.

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data science projects to put on resume

IMAGES

  1. The Perfect Data Science Resume in 2023 (an 8-Step Guide)

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  2. Data Science Manager Resume Examples for 2024

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  3. 16 Data Science Projects with Source Code to Strengthen your Resume

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VIDEO

  1. 15 Data Science Project ideas for your Resume in 2024

  2. 7 Data Science Resume Projects

  3. 5 Data Science Project Ideas for Resume (2024)

  4. My Favorite Data Science Resume Project

  5. 3 Data Science Projects For Resume

  6. Top 10 Data Science Projects for Building Your Portfolio

COMMENTS

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    In this data science project idea, we'll use Deep Learning and the Keras library for classification. Language: Python. Dataset/Package: IDC_regular. 3.6 Traffic Signs Recognition. Achieve accuracy in self-driving cars technology with Data Science Project on Traffic Signs Recognition using CNN with Source Code

  2. The Perfect Data Science Resume in 2023 (an 8-Step Guide)

    Step #1: Keep Data Science Resumes to One Page. The challenge is to be both thorough and concise. A good resume should only be one page long (even if you have twenty years of relevant experience for the job you're applying to). There's a good reason for this. Hiring managers receive hundreds of resumes.

  3. 8 Data Science Projects to Build Your Resume

    A well-written resume is the most critical component of getting an interview for a job as a data scientist. A good data science resume should be brief -- typically, just one page long, unless the applicant has many years of experience. The sections of the data science resume should include: Resume objective. Experience. Education. Certifications.

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    Here are 10 data analyst project ideas that may inspire you to create an impressive program or design for your resume: 1. Classification project. Working on a classification project provides an excellent opportunity to learn how to use machine learning algorithms to group new data points into established categories.

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    1.7 Leaf Disease Detection. Data Science Project Idea: Disease detection in plants plays a very important role in the field of agriculture. This Data Science project aims to provide an image-based automatic inspection interface. It involves the use of self designed image processing and deep learning techniques.

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    Here are five projects that moved the needle for me and paved the path into a year-long internship and two full-time offers. 1. The project that landed me an internship. This was one of the first ...

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    Thanks for reading! If your resume needs a boost, consider doing one of these 7 data science projects to make it stand out. From building an artificial neural network or NLP algorithm to analyzing customer feedback and product reviews for insights, there are many ways that these projects can show your capabilities with data science.

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    To enhance the model's accuracy, it is ideal to use climatological data to find out the common periods and seasons for wildfires. Source Code - Detecting Forest Fire. 3. Detection of Road Lane Lines. A Live Lane-Line Detection Systems built-in Python language is another Data Science project idea for beginners.

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    Key takeaways. The components of your project description that you need on your resume include the objective/goal of the data analysis, your role in the project, a description of the data you used, a list of the models and tools you used, a link to your code repository, and a short discussion of the analysis results.

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    What to Include in Your Data Scientist Resume. In your data science resume, include a profile, work experience, education, skills, achievements, and extras. Profile: A strong profile (also called a summary or objective) will help your data science resume stand out. Your profile should tell a story. Include a brief description of why you are a ...

  17. 15 Data Science Projects that Will Land You a Job in 2023

    15 Data Science Projects that Will Land You a Job in 2023. Building these projects can help you catch up on the upcoming trends of the industry and make your resume shine out. Getting into the dynamic field of data science requires you to catch up and build on the trends of the industry. Building your portfolio is the right direction for it and ...

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  19. 5 Data Engineering Projects To Add To Your Resume

    Project 2. To engage with some new technologies, you should try a project like sspaeti's 20 minute data engineering project. The goal of this project is to develop a tool that can be used to optimize your choice of house/rental property. This project collects data using web scraping tools such as Beautiful Soup and Scrapy.

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    Project 2. In this project, you will explore an Uber-like dataset. It was created by Darshil who makes, probably the best data engineer project videos on Youtube. This currently being the most ...

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    This program covers everything from basic concepts to advanced data techniques. R projects do more than beef up your portfolio. They sharpen your problem-solving skills and prepare you for real data science challenges. Start with a project that interests you and matches your current skills. Then, step by step, move to more complex problems.

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    Top 10 Data Science Projects that strengthens your resume ... Top 10 Data Science Projects that strengthens your resume. code. New Notebook. table_chart. New Dataset. tenancy. New Model. emoji_events. New Competition. corporate_fare. New Organization. No Active Events. Create notebooks and keep track of their status here. add New Notebook.

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