data science projects to put on resume

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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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Top Python Projects to Boost Your Data Science Resume

Introduction

In the fast-evolving field of data science, showcasing your practical skills can set you apart from other candidates. Python, being a cornerstone programming language in data science, offers a broad spectrum of project opportunities that can significantly enhance your resume. This article explores essential Python projects that every data scientist candidate should consider for their portfolio.

• Key Highlights

Importance of Python in data science

Projects that demonstrate analytical and programming skills

Real-world applications of Python projects

Strategies for selecting and presenting projects on your resume

Tips for documenting and sharing your Python projects

• Understanding the Role of Python in Data Science

Python's simplicity and versatility make it a preferred language for data science. This section delves into why Python holds a pivotal role in the data science domain, exploring its popularity and the essential libraries that power data science projects across the globe.

Python’s Popularity and Applications

Python has emerged as a titan in the data science landscape, thanks to its straightforward syntax and powerful libraries. This programming language's popularity stems not only from its ease of learning but also from its vast ecosystem that supports various data science applications. From startup prototypes to complex machine learning algorithms in multinational corporations, Python finds its utility across the board.

Practical applications of Python in data science include, but are not limited to:

• Automating tasks : Python scripts can automate the mundane data processing tasks that are time-consuming, enabling data scientists to focus on analysis.
• Data analysis and wrangling : With libraries like pandas , Python is instrumental in cleaning, transforming, and analyzing data.
• Machine Learning : Python's scikit-learn library is a go-to for implementing machine learning algorithms efficiently.
• Deep Learning : Frameworks such as TensorFlow and PyTorch enable building complex neural networks for tasks like image and speech recognition.

These applications underscore Python's versatility and why it's the preferred choice for data science projects.

Key Python Libraries for Data Science

Python's strength in data science is significantly attributed to its powerful libraries that cater to different phases of a data science project. Here's a rundown of essential Python libraries and their applications:

• NumPy : Ideal for numerical computing, it provides support for complex mathematical operations and large array processing.
• pandas : A cornerstone for data manipulation and analysis, pandas offer data structures and operations for manipulating numerical tables and time series.
• Matplotlib : This plotting library is perfect for creating static, interactive, and animated visualizations in Python.
• scikit-learn : A tool for predictive data analysis, scikit-learn is versatile in handling classification, regression, clustering, and dimensionality reduction tasks.

Each of these libraries has unique features making them indispensable tools in a data scientist's arsenal. For instance, pandas can be used to merge, reshape, and pivot datasets, while Matplotlib aids in visualizing the data, allowing for insights at a glance. NumPy accelerates the computation process, and scikit-learn offers a straightforward approach to applying machine learning algorithms. Together, they form the backbone of Python's application in data science, enabling professionals to tackle a wide range of data challenges efficiently.

• Project Ideas to Showcase Analytical Skills

In the realm of data science, showcasing your analytical prowess is paramount. This section uncovers a suite of project ideas that not only highlight your ability to dissect and interpret complex datasets but also demonstrate your mastery in leveraging Python for insightful data analysis. From visualizing data narratives to unraveling patterns through statistical methodologies, these projects are your ticket to a standout data science resume.

Crafting Data Visualization Projects

Data visualization is an art and science, pivotal for communicating complex analyses effortlessly. Python, with its rich library ecosystem, offers unparalleled tools for crafting vivid, interactive visual narratives. Here are some project ideas that can significantly enhance your data storytelling capabilities:

• Time Series Analysis Visualization : Dive into financial datasets or climate patterns to depict trends, seasonality, and outliers. Tools like Matplotlib and Seaborn can help illustrate these time-bound nuances.
• Geospatial Data Mapping : Leverage Geopandas alongside Folium to map out geographical datasets. Whether it's visualizing global trade routes or the spread of a pandemic, geospatial visuals can offer profound insights.
• Interactive Dashboards with Dash/Plotly : Construct dashboards that allow users to explore data through interactive elements. Retail sales data, sports statistics, or even election results can be made engaging and exploratory.

For each project, ensure to start with a compelling dataset and a clear question you aim to answer. Documentation and narrative are key; weave a story around your data to make your visualizations not just seen, but also understood and remembered.

Embarking on Statistical Analysis Projects

Statistical analysis stands at the core of data science, enabling the extraction of meaningful insights through rigorous methodologies. Python's statistical ecosystem, spearheaded by libraries such as SciPy and StatsModels , provides a robust foundation for conducting sophisticated analyses. Here are project ideas that can showcase your statistical acumen:

• A/B Testing for Website Optimization : Utilize historical website data to conduct A/B tests, aiming to improve user engagement or conversion rates. This project not only demonstrates your grasp of experimental design but also your ability to impact business outcomes directly.
• Market Basket Analysis : Implement the Apriori algorithm to uncover associations between products from retail transaction data. Such analyses can inform cross-selling strategies and product placement decisions, vital for retail analytics.
• Econometric Modeling : Explore economic datasets to model relationships between variables using regression analysis. Projects can range from predicting housing prices to analyzing the impact of policy changes on economic indicators.

The key to a successful statistical project is in framing a clear hypothesis and employing the appropriate statistical tests to validate your assumptions. Comprehensive documentation that details your methodology, findings, and implications will make your project stand out to potential employers.

• Showcasing Machine Learning Mastery with Python Projects

In the rapidly evolving field of data science, machine learning stands out as a critical competency. Mastering machine learning with Python not only demonstrates your analytical prowess but also your ability to predict and influence future trends. This section outlines project ideas that spotlight your machine learning skills through Python, offering a blend of predictive modeling and natural language processing projects.

Crafting Predictive Models with Python

Predictive modeling projects are the cornerstone of showcasing your machine learning expertise. These projects involve analyzing historical data to forecast future events, which is invaluable across various industries.

• Stock Market Prediction: Use Python libraries like pandas for data manipulation and scikit-learn for building regression models to predict stock prices. Incorporate time series analysis to enhance your model's accuracy.
• Customer Churn Prediction: Develop a model that predicts customer churn for businesses. This involves analyzing customer behavior data to identify patterns that precede churn, enabling businesses to take preemptive action.

For a detailed guide on building a predictive model, consider exploring resources such as Towards Data Science . Remember, clearly documenting your methodology and findings, and hosting your code on platforms like GitHub, will significantly boost your project's professional appeal.

Exploring Natural Language Processing (NLP) with Python

Natural Language Processing (NLP) projects leverage Python to analyze and interpret vast amounts of text data. These projects demonstrate your ability to extract meaningful insights from unstructured data, a highly sought-after skill in today’s data-driven world.

• Sentiment Analysis: Create a sentiment analysis model to gauge public sentiment from social media posts or product reviews. Utilize libraries like nltk or spaCy for text processing and sentiment classification.
• Chatbot Development: Build a Python-based chatbot that understands and responds to human queries. This project tests your skills in both NLP and machine learning, challenging you to implement models that can understand context and nuances in language.

Projects in NLP not only showcase your technical capabilities but also your creativity in problem-solving. Resources such as Natural Language Processing with Python provide excellent starting points for diving into NLP projects. Remember, a well-documented project, shared on platforms like GitHub, can serve as a powerful testament to your skills.

• Real-World Python Projects to Elevate Your Data Science Resume

In the bustling field of data science, showcasing your proficiency in Python through real-world projects can set you apart. This section dives into practical project ideas that not only demonstrate your Python skills but also solve tangible problems, significantly boosting your resume.

Mastering Web Scraping with Python for Comprehensive Data Collection

Web scraping is a potent tool in a data scientist's arsenal, allowing for the extraction of vast amounts of data from the web. Utilizing libraries such as BeautifulSoup and Scrapy , you can collect data that's crucial for market analysis, sentiment analysis, and more.

For instance, scraping product reviews from e-commerce platforms can provide invaluable data for sentiment analysis, helping businesses understand customer satisfaction. Similarly, gathering financial data from various sources can aid in market trend analysis.

A practical application could be building a scraper to collect real estate listings to analyze housing market trends. This project not only demonstrates your ability to gather data but also your understanding of its practical applications in real-world scenarios.

Remember, while web scraping is powerful, it's essential to adhere to legal and ethical standards. Always check a website's robots.txt file and ensure you're compliant with their terms of service.

Designing a Recommendation System with Python

Recommendation systems are at the heart of the user experience in many popular platforms like Netflix and Amazon, making them an excellent project to showcase on your resume. By leveraging Python's machine learning libraries, such as scikit-learn and TensorFlow , you can create systems that analyze user behavior and preferences to suggest relevant items.

A project idea could involve developing a recommendation system for a bookstore, suggesting books based on a user's previous purchases and browsing history. This project not only highlights your machine learning skills but also your ability to apply those skills to enhance user engagement and satisfaction.

For a more in-depth project, you could incorporate natural language processing (NLP) techniques to analyze book reviews and ratings, further refining your recommendations. This demonstrates not only your technical proficiency but also an understanding of how to leverage data to drive business value.

Such projects are not only technically challenging but also highly relevant in today's data-driven market, making them perfect additions to your data science portfolio.

• Finalizing and Presenting Your Projects

In the realm of data science, the culmination of your hard work often materializes in the form of projects. However, the journey doesn't end with just completing these projects; how you finalize and present them can make a significant difference. This segment offers adept advice on documenting and sharing your projects, ensuring they stand out to potential employers.

Documenting Your Projects

Documenting your projects is not just about ticking a box; it's an opportunity to narrate the story of your project. Clear, comprehensive documentation ensures that your project is accessible, understandable, and usable by others. Here are some tips to elevate your project documentation:

Start with a README : Begin with an engaging README file that outlines the project scope, objectives, and outcomes. Use Markdown for formatting to make it visually appealing.

Include Comments in Your Code : Making your code self-explanatory with comments can significantly aid understanding. Brief comments explaining the logic behind crucial code segments can be very helpful.

Use Jupyter Notebooks : For Python projects, Jupyter Notebooks can be a powerful tool to combine code, outputs, and narrative in a single document. This can make your project more interactive and easier to grasp.

Provide Installation and Running Instructions : Ensure that anyone trying to replicate your project knows exactly how to set it up. Include details about dependencies, environment setup, and execution instructions.

Remember, the goal is to make your project as approachable and understandable as possible. Good documentation not only demonstrates your technical abilities but also your communication skills, a crucial asset in data science.

Using GitHub to Showcase Your Work

GitHub has become the de facto portfolio platform for developers and data scientists alike. It's not just a repository to store your projects; it's a showcase of your coding journey. Here’s how to make the most of GitHub for your data science projects:

Create a Clean Repository for Each Project : Each project should have its own repository with a clear, descriptive name. This makes it easier for potential employers to navigate your work.

Make Use of GitHub Pages : GitHub Pages allows you to turn your project repositories into sleek websites. This is especially useful for projects like data visualizations or interactive applications.

Leverage the 'README' File : The README file is your first interaction with visitors. Use it to explain your project, what problem it solves, and how it works. Incorporating visuals or links to live demos can be very engaging.

Include a Link to Your GitHub on Your Resume : Make it easy for employers to find your work. Including a direct link to your GitHub profile on your resume or LinkedIn profile can increase visibility.

Remember, your GitHub profile is an extension of your resume. Keeping it organized, updated, and rich with interesting projects can significantly boost your chances of catching an employer's eye. For more insights on optimizing your GitHub presence, consider exploring resources like GitHub's own guides .

Python projects not only demonstrate your technical skills but also your problem-solving capabilities and creativity. Selecting the right projects for your resume and presenting them effectively can significantly impact your job search success as a data scientist candidate. Embrace the challenge and let your projects speak volumes about your capabilities.

Q: Why are Python projects important for a data scientist's resume?

A: Python projects demonstrate practical skills, problem-solving abilities, and creativity, which are crucial for distinguishing yourself as a data scientist candidate. They provide tangible evidence of your expertise and can significantly enhance your resume.

Q: What type of Python projects should I include on my resume?

A: Include projects that showcase your analytical skills, machine learning proficiency, and ability to apply Python in solving real-world problems. Projects involving data visualization, statistical analysis, predictive modeling, and natural language processing are highly recommended.

Q: How can I showcase my Python projects to potential employers?

A: You can showcase your Python projects by documenting them comprehensively and sharing them on platforms like GitHub. Ensure your documentation is clear and includes an overview, objectives, methodologies, results, and conclusions. A well-maintained GitHub repository can act as a portfolio for potential employers.

Q: What are some key Python libraries I should be familiar with for data science projects?

A: Key Python libraries for data science projects include NumPy for numerical computations, pandas for data manipulation, Matplotlib for data visualization, and scikit-learn for machine learning. Familiarity with these libraries can significantly enhance the quality of your projects.

Q: How do I select the right Python projects for my resume?

A: Select projects that align with your career goals and the job requirements you're targeting. Projects should demonstrate a breadth of skills and a depth in areas where you specialize or wish to specialize. Consider projects that solve real-world problems or showcase innovative solutions.

Q: Can working on Python projects improve my chances of getting hired as a data scientist?

A: Absolutely. Working on Python projects not only improves your technical skills but also demonstrates your ability to apply those skills to solve complex problems. This practical experience is highly valued by employers and can significantly improve your chances of getting hired as a data scientist.

Q: How important is documenting my Python projects for my resume?

A: Documenting your Python projects is crucial for your resume. It allows you to communicate the significance of your work, the problems you solved, and how you approached them. Good documentation makes it easier for potential employers to understand your projects and assess your skills.

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Data Science Projects for Boosting Your Resume

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.

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). 

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:

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) :

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.

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The Junior Data Scientist's First Month

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. 

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. 

Related articles

• How to make an entry level data science resume
• How to explain machine learning projects in resumes

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18 Data Scientist Resume Examples for 2024

• Data Scientist Resume
• Data Scientist Resumes by Experience
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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

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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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 10 fresh and  free Google Docs resume templates  that can make your  resume-building  blues go away!.

NLP Data Scientist Resume

• 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

• 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

• 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

• 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

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

Data Science Director Resume

• 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
• Data Engineer
• Computer Science

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.

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.

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.

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.

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.

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:

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.

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.

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.

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.

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.

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.

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

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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• Data Science
• Data Analysis
• Data Visualization
• Machine Learning
• Deep Learning
• Computer Vision
• Artificial Intelligence
• AI ML DS Interview Series
• AI ML DS Projects series
• Data Engineering
• Web Scrapping

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

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• Top 10 VS Code Extensions For Data Science
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• Top 10 Java Projects With Source Code [2024]
• Top 10 DevOps Projects with Source Code [2024]
• 100+ Machine Learning Projects with Source Code [2024]
• Top 8 Free Dataset Sources to Use for Data Science Projects
• Top 10 Data Science Project Ideas for Beginners in 2024
• Top 10 Data Science Companies in 2024
• Top 10 Data Science Competitions To Consider in 2024
• Best Data Science Training Program in 2024
• Top 10 Data Science Skills to Learn in 2024
• Data Science Project Scope and Its Elements
• Top Data Science Trends You Must Know in 2024

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.

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+ Machine Learning Projects for Resume with Source Code

Machine learning projects for resume that you can add to show how your machine learning skills and experiences fit into the ML job role you're applying for.

Sending out the exact old traditional style data science or machine learning resume might not be doing any favours in your machine learning job search. With cut-throat competition in the industry for high-paying machine learning jobs, a boring cookie-cutter resume might not just be enough. What if we told you there is a simple addition to your machine learning engineer resume to increase your chances of landing a lucrative ML engineer job. 

You would add it in a jiffy, right?

Well, yes, there is. All you need to do is highlight different types of machine learning projects on your resume. 

The best way to showcase you have the required machine learning skills is to highlight how you’ve mastered those skills practically.

Classification Projects on Machine Learning for Beginners - 1

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Table of Contents

Machine learning projects for resume - a must-have to get hired in 2023, machine learning projects for resume - the different types to have on your cv, machine learning project ideas for resume, how to list machine learning projects on resume, faqs on machine learning projects for resume.

Machine Learning and Data Science have been on the rise in the latter part of the last decade. Thanks to innovation and research in machine learning algorithms, we can seek knowledge and learn from insights that hide in the data. Data Engineers, Data Scientists, Data Architects have become significant job titles in the market, and the opportunities keep soaring.

Machine Learning Trends in Recent Years

Deep Learning Trends in Recent Years

With the global machine learning job market projected to be worth $31 billion by the end of 2024 and fierce competition in the industry, a machine learning project portfolio is a must-have. We’ve compiled a list of machine learning projects for a resume to help engineering students or anyone wanting to pursue a machine learning career stand out like GitHub Copilot in the interview. A strong machine learning resume includes different types of machine learning projects. What’s better, we have categorised them into different types, so you can include one project of each type to upgrade your resume with a versatile machine learning skillset for your next ML job interview . Every domain of machine learning presents its challenges and solutions. Hence, having diverse types of machine learning projects for your resume helps recruiters understand your problem-solving approach to various business problems.

A typical machine learning project involves data collection, data cleaning, data transformation, feature extraction, model evaluation approaches to find the best model fitting and hyper tuning parameters for efficiency. Building an ML project from scratch ensures understanding of every step in the machine learning project lifecycle . 

Ace Your Next Job Interview with Mock Interviews from Experts to Improve Your Skills and Boost Confidence!

The ML project types listed below are not exhaustive. Still, they cover diverse types of machine learning projects that can add value to a resume and also one should get hands-on practice before appearing for any data science or machine learning job interview.

1. Machine Learning Projects on Classification

Classification refers to labelling groups of data into known categories or classes. Having ML projects on classification listed on your resume help hiring managers to understand your skills on how to tackle any classification problem end-to-end and select the suitable classification machine learning algorithm . Quite similar to classification is clustering but with the minor difference of working with unlabelled data. Clustering defines the process of grouping together identical objects into individual clusters. So, you can add both classification and clustering related machine learning projects to your resume. 

2. Machine Learning Projects on Prediction 

Predictive modelling often uses historical data to learn and predict the likelihood of an event in the future. Historical data provides insights and patterns for making valuable business predictions—for example, predicting customer churn for an organisation in the next 30 days. Having prediction machine learning projects will help hiring managers to understand how the predictions made by an ML model you built can help organisations take action on a product or a service.

3. Machine Learning Projects on Computer Vision 

Working on hands-on ML projects that employ machine learning algorithms like OpenCV, VGG, ResNet to make sense of real-world objects and environments will show your abilities on how you handle diverse computer vision tasks using machine learning. Some examples of such problems include real-time fruit detection , face recognition , self-driving cars, etc. 

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4. Machine Learning Projects on Natural Language Processing (NLP)

NLP helps computers understand, analyse, and process human language to derive meaningful insights from it. Recognising handwritten letters, speech recognition, text summarization , chatbots, etc, are some projects that you can build to showcase your NLP skills. NLP projects are a treasured addition to your arsenal of machine learning skills as they help highlight your skills in really digging into unstructured data for real-time data-driven decision making.

5. Deep Learning and Neural Network Projects 

Deep learning is a subset of machine learning and one of the most hyped machine learning techniques today. Add deep learning and neural network projects to your resume if you want to showcase your advanced machine learning skills.  Adding deep learning projects to your resume is not a must-have if you’re applying for entry-level machine learning job roles, but they are good to have. 

6. Machine Learning Projects on Time Series Forecasting 

Time series analysis and forecasting is a crucial part of machine learning that engineering students often neglect. Adding machine learning projects from time-series data is an important machine learning skill to have on your resume. Usually, the time element in the data has valuable information for a machine learning model to glean insights, but at times, it could lead to insights that might not be real. Showcasing time-series projects on your resume will highlight your ability to identify the challenges associated with working with time series data and how you tackle those challenges before it’s too late.

Top 30 Machine Learning Projects for Beginners 

New Projects

Let's delve into the different types of ML project ideas in more detail.

1) Machine Learning Projects for Resume on Classification

Classification in machine learning is a technique that classifies data into selected classes or labels. Syntactically or semantically similar data form one particular class. The classes are referred to as collections, labels or targets as well. A typical classification problem is to identify the class for a given data point or instance.  

The principle behind classification problems is to feed large amounts of data to the model and check for prediction accuracy using supervised learning. The idea is to try multiple models and assess the best-suited algorithm for the problem. Since real-world problems are peculiar and characteristic, it is imperative to check for different models before deciding which machine learning model best fits a given use case. 

Machine Learning Project Ideas for Classification Problems

Sentiment Analysis ML Project for Product Reviews 

Sentiment Analysis is the process of identifying the emotions/sentiment in a text. Companies commonly use it to infer social media reviews, customer response and brand reputation. Sentiment analysis segregates the core of the sentiments into four main categories. 

Polarity is the tonality of the text—Ex, negative, positive or neutral.

Emotion signifies happiness, sadness, anger or confusion. 

Urgency means the graveness and criticality of the text, namely urgent or not urgent.

Intention infers whether a customer is interested or not interested.

Pairwise Ranking and Sentiment Analysis of Customer Reviews

The data set for the project contains over 1600 product reviews for medical products, which have been labelled as informative and non-informative. The project’s goal is to perform sentiment analysis on the reviews and rank them in their order of relevance. We start with preprocessing and cleaning the data which is sent to the feature extraction module. After the features are collected and vectorised, we proceed with the classification. Random Forest algorithm is used and performs reasonably well with an accuracy of 85 per cent and above. Finally, pair-wise ranking is done for each review against every other review.

E-Commerce Review Sentiment Analysis Project with Guided Videos

Building Recommender Systems

Recommender systems suggest similar items, places, movies, objects based on a person’s personality, preferences, and likings. Behind the scenes, it groups people with similar tastes together and recommends items from their collective repertoire. Recommender systems are widespread in the industry, with massive applications in eCommerce (Amazon), media(Netflix), and financial institutions (PwC). Content-based filtering and collaborative filtering are the most common techniques employed in the implementation of recommender systems.

Evolution of Machine Learning Applications in Finance : From Theory to Practice

Music Recommendation System on KKbox Dataset

The project aims at predicting if a user will listen to a song again in a period.

KKbox provides a dataset for the project in user-song pairs and the first recorded listening time, along with song and user details. Outliers in the dataset are dropped, and null values are imputed.  The XgBoost algorithm is used to predict the chance of relistening with the highest accuracy. 

Music Recommendation System Project with Guided Videos 

Spam Email Filtering

Spam mail classification labels suspected emails as spam and stop the mails from reaching the mailbox. It checks for the mail content, specific signatures and suspicious patterns to learn about spam mails. There are many techniques available to filter out spam emails -

Content-Based Filtering

Content-Based Filtering creates automatic filtering rules by analysing words, the occurrence of specific words and phrases in the mail. 

Rule-Based Filtering

Rule-Based Filtering uses already created rules to score the message in the text by comparing the regular expression. If a text matches a certain number of threshold rules, it is tagged and spam, thus dropped. The rules are updated periodically to keep up with the variety and novelty in spam messages.

Case-Based Filtering

Case-Based Filtering is among the more popular filtering techniques where spam and non-spam emails are added to a dataset. The dataset goes through the preprocessing stage, and all the emails are converted to two vector classes, spam and nonspam. Learning algorithms are applied to the vectors to classify them as spam and non-spam emails. And finally, testing for new mails occurs on the model. 

Adaptive Spam Filtering

Adaptive Spam Filtering classifies spam emails into various classes. The complete email dataset is divided into groups with emblematic signatures—the algorithm checks for similarities between the incoming mails and the groups and classifies the mails accordingly. 

You can use the day to day email exchanges that are tagged as spam and not spam as the dataset for this ML project idea. The data goes through preprocessing steps like stop words removal and vectorisation, which return the data set in a vector form ready for modelling. The model trains using Logistic regression with an accuracy upwards of 90 per cent. An output class of 1 means that the mail is spam where zero signifies not-spam and one as spam.  Another popular classification algorithm called Naive Bayes Classifier also provides good accuracy. 

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

2) Machine Learning Projects for Resume on Prediction 

A prediction problem in machine learning is the most common, at par in occurrence with classification problems. Predictions take historical data and find the insights and trends hidden in the dataset .  The larger the dataset we have for training, the better and more accurate the prediction algorithm becomes. The current and historical data is taken to build a model that can predict events or trends in the future. The predictions can range from the potential risk of a credit card issue request to calculating the stock prices for a multinational company.

Machine Learning Project Ideas on Prediction Problems 

Sales forecasting.

Forecasting future sales depends on many factors like past sales, seasonal offers, holidays and festivals etc. Future sales also dictate staff requirements and stocking product inventory for future needs. Autoencoders and multivariate models can make a good fit for forecasting prediction problems where time is an added constraint. 

Rossmann Store Sales Prediction Project

The dataset contains historical data from more than 1000 Rossmann drug stores, including customer id, sales, store, state holidays, etc. Missing data points are imputed, and outliers get removed. Data is converted into numerical form by using one-hot encoding for easier manipulation. Stochastic Gradient Descent and Decision Tree regressor algorithms are mainly used in the model. 

Project to Forecast Future Sales of Rossman Store with Guided Video

Weather Forecasting

We tend to look at the weather report multiple times in our daily life. Predicting rainfall is of utmost importance to industries that depend on rains like agriculture . Weather predictions are relatively challenging and better done using Deep Learning algorithms . Even so, traditional ensemble models can offer outstanding results with the need for high resources. 

The project’s data set is featured at Kaggle with information on the date, the average temperature on land and sea, minimum and maximum temperature on land and sea. The previous value replaces null values in the dataset, and date entries are converted to a DateTime object. The Zero-differentiated ARIMA model is used for prediction as, along with being a prediction problem, weather forecasting is also a time series problem. Finally, the accuracy is measured by Akaike Information Criterion. 

Customer Churn Prediction

Customer churn is the behaviour of customers to stop using an organisation’s products or services. Customer churn rate is the rate of people who discontinue paid services in a particular interval of time. Churn is bad for companies as they lose revenue. Churn prediction finds applications in telecom, music and movie streaming services or other subscription-based services. Churn also signifies the health and reputation in the market for a company.

Customer Churn Prediction Analysis for Bank Records

The dataset from the bank records stores customer name, credit score, geography, balance, tenure, gender, etc. Preprocessing, imputing and label encoding are the next steps that occur. The dataset goes through feature extraction at this stage, eliminating less essential fields, making the dataset manageable and more consistent. The Light Gradient Boost Machine or LGBM algorithm provides maximum accuracy and is preferred for this project. Being lightweight, it is suitable in the big production setting of a bank.

Customer Churn Prediction Project with Guided Videos  

3) Machine Learning Projects for Resume on Computer Vision  

Computer Vision combines machine learning with image/vision analysis to enable systems to infer insights from videos and images. For a computer, it becomes quite a challenge to interpret pictures and distinguish the features. While as humans, we have evolved over a long time with our vision as a central characteristic. For humans, using vision to recognise objects around us is almost second nature. Computer Vision offers the possibility for computers to develop the vision to assimilate and comprehend the world around them. 

The main principle involved in computer vision is to break the image into pixels. Pixels are the most fundamental constituents of an image. By recognising the pattern in the pixel pool, computers begin the task of image identification. Equally important is what features we extract from these pixels and how we construct the learning model.

Computer Vision Techniques

Object Detection is the identification of objects in an image. These objects can be any person, thing, animal, or place but need distinctive features that the model uses to recognise and detect the subjects in the photos. Object detection happens through localisation, where a bounding box outlines the object. The object comprises many pixels, and those pixels belong to the same object class. Object detection is used in google photos, where google detects faces from our library of images.

Object Tracking refers to following the path of a particular object in a situation or environment. Stacked Auto Encoders (SAE) and Convolutional Neural Network  Surveillance is an ideal example of object tracking. 

Image Classification is tagging images under a class holding similar photos. An example of image classification is the annoying ‘Not a Robot’ authentication that forces one to select all the traffic lights in the image. 

Image Segmentation

Image segmentation aims to break the image into partitions or segments so that it’s easier to analyse and process the whole picture. There are two types of image segmentation possible, listed as follows:

Instance Segmentation - It recognises each object of the same type as a new object. So an image of three elephants would be categorised into three separate elephant classes, namely, elephant1, elephant2 and elephant3.

Semantic Segmentation - It understands the semantics in the pixels and labels semantically similar objects in the same class. Considering the elephant example from above, pixels in the image of three elephants will get tagged under only one elephant class, namely elephant.

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Machine Learning Project Ideas on Computer Vision

Face recognition.

Face recognition is a non-trivial computer vision problem that recognises faces and clusters them under appropriate classes. Face recognition finds uses in mobile phone applications, surveillance, photo tagging applications, google lens, etc. OpenCV is the most popular library that helps with building models for face recognition. 

Face Recognition System in Python using FaceNet

The dataset for the project is a video from the famous sitcom show called Friends. Frames per second from the video are extracted to form the dataset in which we need to recognise the cast’s faces. A total of 35 images, with seven images for each character, are collected. Haar Cascade Object is used for face detection and extraction, while Convolution Neural Network is used for model training. 

Face Recognition Project using Facenet with Guided Videos

Building an OCR System from Scratch 

Optical character recognition is the technique of identifying the letters and digits in a handwritten document or bill. It extracts the relevant information from the documents and records it in the database. Since handwritings come in numerous styles, OCR needs extensive training and fine-tuning of parameters. 

Building OCR in Python using YOLO and Tesseract

The dataset for the project is created using the Labellmg tool in python to label all the invoices present. After the labelling, we proceed with the YOLOv4 ( you only look once ) algorithm to detect the invoice number, date and total bill amount. Next, Tesseract is used to read/predict text from the detected fields.  We also use image augmentation to expand the dataset to a considerable size if the dataset is small. 

OCR Project Built from Scratch with Guided Videos 

Image Restoration - Denoising Images 

Image restoration is the reconstruction of old images to make them new-like with optimum quality and features. It takes into consideration both spatial information and frequency to replace missing values in a snap. 

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4) Machine Learning Projects for Resume on NLP 

Natural Language Processing is part of machine learning that involves understanding and processing human language, text, and spoken.  Here is a list of prevalent NLP tasks that will help in getting a sense of its wide array of applications:

Speech Recognition

Part of Speech Tagging

Word Sense Disambiguation

Named Entity Recognition

Coreference Resolution

Sentiment Analysis

Natural Language Generation 

NLP Techniques

Natural Language processing uses two effective techniques which differ in their approach to analysing language; they are namely:

• Remove Punctuation Punctuations clutter the data with useless tokens and don't add to the model efficiency. It's best practice to remove them beforehand. 
• Tokenisation is the breaking of sentences into smaller parts that can be either words or combination words. It makes data processing easier and uniform across the whole dataset.
• Lemmatisation is converting words to their most basic form called Lemma. The lemma replaces every other form of the word. For example, learning, learned, learnt, learnable shall be replaced with learning.
• Stemming is the process of dropping the beginnings and ends of words depending on their prefix and suffix. 
• Part of Speech Tagging labels tokens as a verb, adverb, adjective, noun etc., based on the grammatical vocabulary. It helps discern the difference between the noun and adjective forms of the same word if a comment has different meanings. For example, the word sense signifies the five senses and the act of perceiving. 
• Stop Words Removal focuses on deleting all the common stop words like a, an, the, and, like, just that don't add to the concrete meaning of the text.
• Vectorisation or Bag of Words is the process of counting the occurrences of individual words in a text. The count of each word helps in understanding how important the word is to the whole subtext.
• Word Sense Disambiguation identifies different forms/meanings of the exact words depending upon the context of its use and neighbouring terms.
• Word Relationship Extraction tried to infer the relationships between different words in a sentence like a place, subject, object etc.

Machine Learning Project Ideas on NLP

Build a chatbot.

Chatbots are NLP applications that enable us to query details and raise grievances in natural language to receive relevant information. Chatbots are prevalent in the customer service industry, where setting up call centres is cumbersome and not budget-friendly.  An example is the amazon chatbot that helps customers with order information, order cancellation etc. 

Natural Language Processing Chatbot using NLTK

The dataset is conversations from a leave enquiry and application system for the organisation. The textual data is pre-processed using various NLP techniques like lemmatisation, tokenisation, stemming and stop words removal. The occurrence of each word is counted to create a count vector model called a bag of words. You can use algorithms like Naive Bayes Classifier and Decision Tree for modelling. 

NLP Chatbot Project with Guided Videos

Speech Recognition 

Speech recognition is the ability of a machine to understand human language and respond coherently with appropriate data. Speech recognition finds use in our daily life while we use maps, call a friend or translate language all through our voice. Alexa in Amazon Echo and Siri in Apple iPhones are some of the best examples of speech recognition. 

Topic Modelling

Topic modelling is the inference of main keywords or topics from a large set of data. It measures the frequency of a word in the text and its relationship with neighbouring words to extract succinct information. Typical uses are labelling unstructured data into formatted topics. It can also be used in text summarisation problems with minor tweaks to the model. 

Topic Modelling using K-means Clustering on Customer Reviews 

The customer reviews for the project are sourced from Twitter for a particular company.

Data goes through many layers of preprocessing as the twitter reviews are unfiltered and raw. Tokenisation and vectorisation are performed using TD-IDF and count vectoriser. Model training is done using k-means clustering , an unsupervised learning algorithm. The final result is clusters of tweets with different classes that signify the dominant topic in the cluster. 

Topic Modelling for Customer Reviews ML Project with Source Code 

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5) Deep Learning and Neural Networks Projects for Resume

Deep Learning aims at mimicking and simulating human thought patterns by using complex and layered structures called Neural Networks . In simple terms, Deep learning is multiple Artificial Neural Networks connected. Neural networks can accomplish clustering, classification and regression with greater efficiency than traditional machine learning algorithms. 

Deep Learning eliminates the feature extraction process and skips over this step essential to all the traditional machine learning algorithms. These classic algorithms, called flat algorithms , cannot use data without preprocessing or feature extraction. Feature extraction is a detailed and involved process that needs expertise in the problem domain and patience in refining it over time. Deep Learning straight away discards this step and moves on with raw data. Deep Learning can learn and model the problem satisfactorily upon many iterations by tuning the weights using loss functions. 

A brief look at the architecture of a deep learning model

Nodes - A neural network is a collection of primary cells called Nodes. A Node stores arithmetic values like 0.4,2.21 etc

Weights are the branches that connect two nodes. They represent a number that keeps changing over the training time. The process of starting from a set of random weights to arriving with specific values that fits the input data is called Learning.

Loss Function defines the difference between the prediction vector obtained from the Neural network and the actual output vector—the lesser the loss function value, the better the model. 

Back Propagation of Errors pushes the errors back towards the input layer. In the process, it keeps updating the weights in each hidden layer. The principle behind this is that the total error gradient in the output layer is the sum of individual error gradients at each point in the network. 

Gradient Descent is when the weights are tuned using the derivative of the loss function to improve the network. The idea is to bring the weights to a value that spawns the most accurate prediction. 

The nonlinear activation function is applied to the dot product of the previous hidden layer vector and weights connecting the two participating layers. 

A feature vecto r is the input vector that goes into the Neural Network through the input layer. It contains a vectorised form of the input.

Prediction vector is the vector form of the output that the neural network produces.

Input and Output layers - Input and output layers are a neural network’s first and last layers. 

Input Layer is a set of nodes that represent a data point in vector form. For example, for image recognition models, the input layer would be the vectorised version of the image pixels. 

Output Layer denotes the result of the Neural Network. It is again a set of nodes quite like the input layer, but these individual nodes represent the output classes of the problem. For example, in the image recognition problem, the output layer would be nodes corresponding to objects in the image like cars, sheep, women etc.

Hidden Layers are the layers sandwiched between the input and the output layer. All the computations ( like weights tuning ) happen among these layers.

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Deep Learning and Neural Network Project Ideas for Resume

Self-driving autonomous cars.

Autonomous-driving cars can navigate through traffic and control acceleration and speed depending on their environment. Perception, Localisation, Planning, control are the four central ideas in self-driving cars. 

Perception is figuring out the environment and obstacles. 

Planning is the trajectory from point A to point B

Localisation is identifying the current location in the world.

Control relates to steering angle and acceleration. 

Natural Language Translation using Deep Learning 

Language translation is extremely important in international trade, discourses, education and media where two parties interact without any common language. It translates text or speech from one language to another.

For example- Google translate is a google cloud application that offers text translation into various languages. It uses Transalatron to develop the learning model. Neural Nets used are LSTMs and sequenced RNNs with an encoder-decoder model. 

Credit Card Anomaly Detection using Autoencoders 

The project aims at detecting fraudulent credit card transactions so the system can curb them and charge the customer of only the actual transactions. The dataset contains records of credit card transactions that are legal and fraudulent which have been passed through PCA (principal component analysis)analysis to change the data fields into numbers. We also have transaction amount, the time difference between consecutive transactions, and fraudulent transaction each unique credit card. Neural networks and autoencoders are used in conjunction with each other for modelling. And finally, the accuracy of the model is measured using Mean Squared Error (MSE) with the ggplot2 package. 

Credit Card Anomaly Detection Project with Guided Videos

6) Machine Learning Projects for Resume on Time Series Data

Time Series data helps predict an object’s behaviour compared to its older state in time. Time series is a dataset of continuous and periodic observations of the time instances attached to the data itself. Time Series finds use in many prediction scenarios like weather prediction, prediction for the price of an item, sales prediction, etc. It is much like prediction but with an added time constraint or feature, making it an altogether different problem of time-series forecasting. Generally more complex than traditional prediction projects.

Datatypes in Time Series

Time Series Data are observations recorded at different instances in time for a set period. 

Cross-Sectional Data

Data values of more than one variable are gathered at the same time. Thus, freezing or capturing the state of a system as a single entity in time. That is why the word cross-section comes into play, which implies a time-print of the model.

Pooled Data is the mixture of time-series data and cross-sectional data. 

Types of Time Series Modelling

Time series forecasting further divides into two subcategories based on the number of variables in the model, which are as follows:

Univariate Time Series Forecasting is when only one other forecasting variable is present in the model apart from time. For example, in a sales prediction model, the number of sales is the only one that will vary with time.

Multivariate Time Series Forecasting model is one where multiple variables are changing with time. Naturally so, the forecasting depends on these variables as well. For example, the temperature during the day depends on many variables like rainfall, wind, overcast etc. So essentially, a model to predict the temperature would be a candidate for multivariate time series forecasting .  

Overview of Seasonality and Autocorrelation in Time Series Data

Autocorrelation defines the similarity between a time series and its lagged version, showing the relationship between past and present values. Autocorrelation is also called lagged correlation or serial correlation. It ranges between the value of -1 to 1. 

Seasonality signifies periodic fluctuations in the graph of time series. Quite simply, it means that the data in the sequence repeats after a specific time called the period. Seasonality is generally calculated over one financial year.

Time Series Analysis and Forecasting Techniques 

ARIMA or Auto-Regressive Integrated Moving Average combines three models, i.e. ‘AR’, ‘MA’ and ‘I.’

AR shows the evolving variable of interest regressing over its initial values.  

MA shows that the regression error is the linear combination of error term values at previous instances. 

I shows that the data values are replaced by differences in their values from older values. 

Moving Average is so-called because each data point averages the data values before and after in the time series and creates a new time series. Moving Average highlights trends and trends cycle. It is ideal for univariate time series.

Exponential Smoothing creates the new time series by average the weight values from the current time series. A Datapoint in a time series has less weight if it's older in time compared to a recent data point.  The theory being that recent data has more chance of reoccurring again.

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Machine Learning Project Ideas for Resume on Time Series Data

Weather forecasting  .

Weather forecasting is a complex time series problem that uses past weather data and related parameters like wind pressure, overcast, wind speed etc., into account while forecasting future weather. 

The dataset contains the average temperature recorded in 2000 stations over Helsinki for some time. The SARIMA or Seasonal ARIMA model is used to model, and the Root Mean Squared value is used to check the accuracy.

Sales Prediction 

Sales prediction in a company is again a time series problem that considers the month of the year, holidays around and seasons in predicting future sales. The sales data show a cyclic trend in data that repeat every year. The dataset contains product information such as item id, item weight, type of the item, item MRP, etc. The dataset undergoes imputing of null values and one hot encoding. Outliers are identified with boxplot and deleted accordingly. Gradient boost tree and xgboost algorithms are applied for modelling, but the most efficient algorithm turns out to be a neural net with MLPRegressor. 

Project on Bigmart Sales Prediction with Guided Video tutorials

Stock Prediction 

Stock Market prediction depends on the historical stock records, geopolitical environment and company performance in recent times. It is a complicated prediction problem that involves time series along with deep learning.

The data is taken from the EU stock market with fields like the German DAX stock index, UK stock index, etc. We extract the trend and seasonality in the dataset and identify correlations and autocorrelations. Vector Autoregression (VAR) is used for modelling with good accuracy among other algorithms like ARIMA and LSTM.

Time Series Project on Stock Market with Source Code and Explanatory Videos

If you are a recent college graduate or in the final year of graduation, you know how difficult it is to create a data science or machine learning resume without prior work experience. However, adding diverse machine learning projects mentioned above can definitely add credibility to your resume.

It is essential to treat the various types of machine learning problems discussed above as a general guide since each project is unique and needs a precise approach. One can start by learning one project in each category and proceed from there. It is crucial to take note of learnings from each project and list them in the resume. 

Here’s a recommended list of blogs on different types of project ideas for further exploration and reading -

• 8 Newest Projects to Jump-Start the Data Science Journey
• Image Processing Projects
• Deep Learning Projects
• Data Science Projects
• 15 Data Mining Projects Ideas with Source Code for Beginners
• 20 Machine Learning Projects That Will Get You Hired in 2021
• 15 Data Visualization Projects for Beginners with Source Code
• 20 Web Scraping Projects Ideas for 2021
• 8 Healthcare Machine Learning Project Ideas for Practice in 2021
• Access Job Recommendation System Project with Source Code

1) How do you put machine learning projects on your resume?

Machine Learning projects should be brief and to the point on the resume. One can briefly discuss the dataset, model training, libraries used and accuracy by mentioning only the crucial points. 

2) Are Machine Learning projects good for a resume?

Indeed, machine learning projects are great additions to one’s resume. Machine learning is a burgeoning field and adding ML projects to the resume opens up job more opportunities. Candidates who wish to make a career in Machine Learning or Deep Learning need to build a versatile portfolio of ML projects for the resume.

3) Can one do Machine Learning projects in an Internship?

Yes, one can do machine learning projects in internships. In fact, during internships, one learns to build and deploy machine learning projects in real-time. It is an ideal environment to expand one’s experience and knowledge. But it is equally essential to be able to land an internship in the first place. It is best to start learning and practising machine learning projects at our own pace and slowly build an internship resume. By enlisting some prior understanding in machine learning projects, one can increase their chances of landing a machine learning internship. 

4) What projects can I do with Machine learning?

With Machine Learning, one can do many projects depending on the project type and theme.  A good strategy would be to pick one project from each category, as discussed above, for the resume. Face Recognition Project , Sales Prediction projects, Recommendation System Projects , Building a chatbot using NLTk , Spam mail detection project etc., are good choices to get started with gaining hands-on exposure to diverse kinds of problems.

5) How does one write a Data Science project for a resume? 

A data science project for a resume should have a brief introduction followed by a one-line explanation about the dataset and data-cleaning techniques involved. 

Following that, one should write about the models used and the model that produced maximum accuracy.  

It is crucial to remember not to be long-winded in describing the project and mention the significant points. 

In the end, you can conclude by remarking about the learnings obtained during the project and key takeaways.

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• Last updated November 18, 2022
• In Innovation in AI

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

• Published on November 18, 2022
• by Mohit Pandey

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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EMCC STEM Students Pursue Pollinator Projects

Undergrads Study Wildflower Growth; Conduct Native Bee Survey

Estrella Mountain Community College (EMCC) STEM students are busy, busy bees having engaged in Undergraduate Research Experiences, or UREs this semester. Some of our Mountain Lions just wrapped up a study of wildflower growth across different soil types while others are conducting a native bee survey — two things that can’t live without the other.

The wildflower study started last fall when  Quail Forever , a wildlife habitat conservation group, donated a rather large sum of wildflower seeds to EMCC Biology Professor Dr. Catherine Parmiter to use in her classes. They couldn’t have come at a better time as her colleague, Professor Thasanee Morrissey, who also teaches biology and is the Program Analyst for the STEM Center, just happened to be looking for a research opportunity for her students.

They decided to create a URE for five of their students and the Pollinator-Wildflower Research Initiative was born. The goal of the initiative was to determine which type of soil wildflowers would grow best in, with the understanding that more healthy wildflowers attract pollinators such as bees.

First, with the help of their Life Sciences Division colleagues Drs. Neil Raymond, Rachel Smith, and Jarod Raithel, along with the Facilities Department, an area was cleared next to the EMCC Community Teaching Garden where they constructed 16 research plots with four different soil types — native soil, pea gravel, compost, and sand. Next, they asked the MakerSpace to create some appealing signage to mark off the area. Then they planted nine different varieties of wildflower seeds and turned on the irrigation. After that, they monitored the plots weekly and kept track of the plants’ growth with written observations and digitized images.

Natalia Quinones, one of Dr. Parmiter’s students who is graduating this spring with an Associate in Biological Sciences and then transferring to  Arizona State University (ASU) to study microbiology, said one of the reasons she signed up for the URE was to boost her resume.

“I hope that this experience will allow me to join other research projects when I transfer to ASU,” she said.

Dr. Parmiter said the selection process for research opportunities at the university level is very competitive.

“Gaining research experience at the pre-Associate Degree level is essential for students such as Natalia as she navigates her transfer to ASU and later to medical school,” Dr. Parmiter said. 

For this URE, Natalia and her lab partner were responsible for identifying the types of flowers in each substrate of soil and measuring the nitrogen, phosphorus, potassium, and pH content in each plot. 

“I learned more about plant growth and development,” Natalia said. “I gained more knowledge and new vocabulary about the subject. And I learned how to edit and rewrite procedures.”

Dr. Parmiter said Natalia’s field observations and attention to detail were an asset to the team.

“She is an excellent student researcher,” Dr. Parmiter said.

Natalia also works as a part-time lab technician in EMCC’s Life Science Lab, another gold star on her resume.

“I started as a student worker in September 2022 and the lab technicians were always patient and allowed me to make mistakes and learn from them,” she said. “And since they knew I wanted to pursue an education in microbiology, they educated and taught me skills that would apply to my field of study.”

Students who participated in the Pollinator-Wildflower Research Initiative will earn  Western Alliance to Expand Student Opportunities (WAESO) scholarships after they submit their research summaries.

“This scholarship is encouragement for all of the hard work that has gone into this project,” Natalia said. “It also shows that the school supports undergraduate students to work outside the classroom and gain hands-on experiences.”

Cierra Herrera, one of Professor Morrisey’s students who participated in the Pollinator-Wildflower Research Initiative, is also big on hands-on experiences. 

“I learn best when I am doing, and I learned a lot,” Cierra said. “I love to learn and put that knowledge into practice and that is exactly what UREs do.”

Cierra, who is also one of EMCC’s  Animal Ambassadors , will graduate this spring and then transfer to the  University of Hawaiʻi at Mānoa . She plans to double major in Animal Science and either Plant and Environmental Protection Services or Marine Biology.

“I’ve always been caring and conserving before I even knew what that meant,” she said. As unusual as it might sound for a 10-year-old, I hated wasting paper, always recycled, loathed littering, and it always hurt me to see animals suffering, especially because of us, and when we can do something about it. As I continued to go to school and learned more about biology, endangered species, and why they are being endangered, there was no doubt in my mind that I wanted to help these animals.”

Naturally, when Cierra heard about the native bee survey URE, she signed up for that one, too. A perfect complement to the Pollinator-Wildflower Research Initiative, the EMCC Native Bee Project officially kicked off in March. It’s part of a collaborative effort with community colleges in Arizona and California conducting surveys to find out how many different types of bees exist across the two states, something that is currently unknown.

“One out of every three bites of your food you owe to bees,” Dr. Raithel said. “We don’t even have a baseline to know how many bees we have. They are crucial to our survival, yet we know so little about them.”

The EMCC Native Bee Project began over spring break with Drs. Parmiter, Raithel, and Smith spending four days at the  College of the Canyons in Santa Clarita, Calif., learning how to identify, or “key,” native bees so that they could pass that knowledge on to their URE students. Since then, they have begun teaching their students how to catch, clean, dry, pin, key, and photograph native bees caught on and around campus. It’s a lengthy and sometimes nerve-racking process, but for Cierra, the keys are the bee’s knees.

“Looking at the bee under the microscope is my favorite part,” she said. “They are majestic creatures and so beautiful. It is crazy to see the variation of bees in our lab! They are all so unique.”

The  National Science Foundation -funded native bee URE will last three years with six students participating each semester. The data collected will be verified and entered into  Symbiota , a public database, and each bee will have an identification number that corresponds to the student who keyed it.

“It is mind-blowing just thinking about the fact that a native bee that I, myself, keyed will go into a national database with my name!” Cierra said. “That’s absolutely surreal to me, but it is really happening. It makes me a little emotional just thinking about it because I see it as a big deal and I’m only 20 years old and this is happening along with my fellow peers. I can only think about my future and what it has in store for me.”

Cierra’s professors describe her as a problem solver who never hesitates to roll up her sleeves and dive into the action.

“She was like our wildflower research group’s secret weapon — always diving fearlessly into problems and asking all the right questions,” Professor Morrisey said. “With her sharp critical thinking skills, she was like the Sherlock Holmes of our research team! But what’s even better is her team spirit — she’s the ultimate collaborator, bringing fresh ideas to the table.”

Professor Morrisey’s students wrapped up their wildflower growth URE and presented their findings at the  Arizona-Nevada Academy of Science Annual Meeting on April 13 at Glendale Community College. 

“They did great and had a great experience at their first science conference,” Professor Morrisey said.

Cierra said she was nervous but ultimately enjoyed herself.

“It was really good!” she said. “One of the judges said our poster was eye-catching and easy to follow. He was really happy with our experiment in the design aspect — how we eliminated a lot of bias, controlled all of our variables well, and the quadrat sampling. It was really rewarding to hear that feedback.”

Are you an Estrella Mountain Community College student who would like to join the EMCC Native Bee Project or any other STEM Undergraduate Research Experience? Email Dr. Catherine Parmiter at  [email protected] .