physics phd to data science

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PhD in Physics, Statistics, and Data Science

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Many PhD students in the MIT Physics Department incorporate probability, statistics, computation, and data analysis into their research. These techniques are becoming increasingly important for both experimental and theoretical Physics research, with ever-growing datasets, more sophisticated physics simulations, and the development of cutting-edge machine learning tools. The Interdisciplinary Doctoral Program in Statistics (IDPS)  is designed to provide students with the highest level of competency in 21st century statistics, enabling doctoral students across MIT to better integrate computation and data analysis into their PhD thesis research.

Admission to this program is restricted to students currently enrolled in the Physics doctoral program or another participating MIT doctoral program. In addition to satisfying all of the requirements of the Physics PhD, students take one subject each in probability, statistics, computation and statistics, and data analysis, as well as the Doctoral Seminar in Statistics, and they write a dissertation in Physics utilizing statistical methods. Graduates of the program will receive their doctoral degree in the field of “Physics, Statistics, and Data Science.”

Doctoral students in Physics may submit an Interdisciplinary PhD in Statistics Form between the end of their second semester and penultimate semester in their Physics program. The application must include an endorsement from the student’s advisor, an up-to-date CV, current transcript, and a 1-2 page statement of interest in Statistics and Data Science.

The statement of interest can be based on the student’s thesis proposal for the Physics Department, but it must demonstrate that statistical methods will be used in a substantial way in the proposed research. In their statement, applicants are encouraged to explain how specific statistical techniques would be applied in their research. Applicants should further highlight ways that their proposed research might advance the use of statistics and data science, both in their physics subfield and potentially in other disciplines. If the work is part of a larger collaborative effort, the applicant should focus on their personal contributions.

For access to the selection form or for further information, please contact the IDSS Academic Office at  [email protected] .

Required Courses

Courses in this list that satisfy the Physics PhD degree requirements can count for both programs. Other similar or more advanced courses can count towards the “Computation & Statistics” and “Data Analysis” requirements, with permission from the program co-chairs. The IDS.190 requirement may be satisfied instead by IDS.955 Practical Experience in Data, Systems, and Society, if that experience exposes the student to a diverse set of topics in statistics and data science. Making this substitution requires permission from the program co-chairs prior to doing the practical experience.

• IDS.190 – Doctoral Seminar in Statistics and Data Science ( may be substituted by IDS.955 Practical Experience in Data, Systems and Society )
• 6.7700[J] Fundamentals of Probability or
• 18.675 – Theory of Probability
• 18.655 – Mathematical Statistics or
• 18.6501 – Fundamentals of Statistics or
• IDS.160[J] – Mathematical Statistics: A Non-Asymptotic Approach
• 6.C01/6.C51 – Modeling with Machine Learning: From Algorithms to Applications or
• 6.7810 Algorithms for Inference or
• 6.8610 (6.864) Advanced Natural Language Processing or
• 6.7900 (6.867) Machine Learning or
• 6.8710 (6.874) Computational Systems Biology: Deep Learning in the Life Sciences or
• 9.520[J] – Statistical Learning Theory and Applications or
• 16.940 – Numerical Methods for Stochastic Modeling and Inference or
• 18.337 – Numerical Computing and Interactive Software
• 8.316 – Data Science in Physics or
• 6.8300 (6.869) Advances in Computer Vision or
• 8.334 – Statistical Mechanics II or
• 8.371[J] – Quantum Information Science or
• 8.591[J] – Systems Biology or
• 8.592[J] – Statistical Physics in Biology or
• 8.942 – Cosmology or
• 9.583 – Functional MRI: Data Acquisition and Analysis or
• 16.456[J] – Biomedical Signal and Image Processing or
• 18.367 – Waves and Imaging or
• IDS.131[J] – Statistics, Computation, and Applications

Grade Policy

C, D, F, and O grades are unacceptable. Students should not earn more B grades than A grades, reflected by a PhysSDS GPA of ≥ 4.5. Students may be required to retake subjects graded B or lower, although generally one B grade will be tolerated.

Unless approved by the PhysSDS co-chairs, a minimum grade of B+ is required in all 12 unit courses, except IDS.190 (3 units) which requires a P grade.

Though not required, it is strongly encouraged for a member of the MIT  Statistics and Data Science Center (SDSC)  to serve on a student’s doctoral committee. This could be an SDSC member from the Physics department or from another field relevant to the proposed thesis research.

Thesis Proposal

All students must submit a thesis proposal using the standard Physics format. Dissertation research must involve the utilization of statistical methods in a substantial way.

PhysSDS Committee

• Jesse Thaler (co-chair)
• Mike Williams (co-chair)
• Isaac Chuang
• Janet Conrad
• William Detmold
• Philip Harris
• Jacqueline Hewitt
• Kiyoshi Masui
• Leonid Mirny
• Christoph Paus
• Phiala Shanahan
• Marin Soljačić
• Washington Taylor
• Max Tegmark

Can I satisfy the requirements with courses taken at Harvard?

Harvard CompSci 181 will count as the equivalent of MIT’s 6.867.  For the status of other courses, please contact the program co-chairs.

Can a course count both for the Physics degree requirements and the PhysSDS requirements?

Yes, this is possible, as long as the courses are already on the approved list of requirements. E.g. 8.592 can count as a breadth requirement for a NUPAX student as well as a Data Analysis requirement for the PhysSDS degree.

If I have previous experience in Probability and/or Statistics, can I test out of these requirements?

These courses are required by all of the IDPS degrees. They are meant to ensure that all students obtaining an IDPS degree share the same solid grounding in these fundamentals, and to help build a community of IDPS students across the various disciplines. Only in exceptional cases might it be possible to substitute more advanced courses in these areas.

Can I substitute a similar or more advanced course for the PhysSDS requirements?

Yes, this is possible for the “computation and statistics” and “data analysis” requirements, with permission of program co-chairs. Substitutions for the “probability” and “statistics” requirements will only be granted in exceptional cases.

For Spring 2021, the following course has been approved as a substitution for the “computation and statistics” requirement:   18.408 (Theoretical Foundations for Deep Learning) .

The following course has been approved as a substitution for the “data analysis” requirement:   6.481 (Introduction to Statistical Data Analysis) .

Can I apply for the PhysSDS degree in my last semester at MIT?

No, you must apply no later than your penultimate semester.

What does it mean to use statistical methods in a “substantial way” in one’s thesis?

The ideal case is that one’s thesis advances statistics research independent of the Physics applications. Advancing the use of statistical methods in one’s subfield of Physics would also qualify. Applying well-established statistical methods in one’s thesis could qualify, if the application is central to the Physics result. In all cases, we expect the student to demonstrate mastery of statistics and data science.

At home, abroad, working, interning?  Wherever you are this summer, contact OCS or make an appointment for a virtual advising session. We are available all summer! 

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Transitioning from Physics to Data Science

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Mohammad Soltanieh-ha, physics Ph.D., data scientist, and faculty of Information Systems at Boston University, shares his personal experience along with helpful resources for those making a transition from Physics background into data science.

Video: APS Physics YouTube Channel

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• NATURE CAREERS PODCAST
• 07 August 2019

Working Scientist podcast: Career transitions from physics to data science

• Julie Gould 0

Julie Gould is a freelance science writer in London.

You can also search for this author in PubMed   Google Scholar

Credit: marrio31/Getty

Bitten by the business bug: Three data scientists tell Julie Gould about their roles.

In 2013, Kim Nilsson co-founded Pivigo, a training company to prepare researchers for data science careers. She tells Julie Gould how and why she moved into business.

Nilsson's Pivigo colleague Deepak Mahtani quit academia after completing a PhD in astronomy. What is his advice to someone looking to move into data science? "There are three main things you should do. Learn about the programming languages Python or R, read up about machine learning, and understand a bit about SQL," he says.

Lewis Armitage's PhD at Queen Mary Unversity London took him to CERN, the European Organization for Nuclear Research. But he craved a better work-life balance and a move which played to his data science skills. Now he is a data analyst for consumer behaviour consultancy Tsquared Insights, based in Geneva, Switzerland.

doi: https://doi.org/10.1038/d41586-019-02408-8

Julie Gould:

Hello, I’m Julie Gould and this is Working Scientist, a Nature Careers podcast. This is the third part of our series on careers in physics, where we’re exploring transitions. Last week we heard from Elizabeth Tasker, a UK-born astrophysicist who transitioned to Japan and now combines her love of research with her love of science communication. But in this episode, we’re exploring a slightly different type of transition – from physics to data science. It’s a topic that I’ve been keen to explore because physicists are often coveted by industry for their skills in data science, but there are so many more people graduating from data science-focused graduate degrees that I wonder if there’s still a place for physicists in this industry. So, this is exactly why Kim Nilsson set up Pivigo in 2013 – initially, to help academics transition from academia to industry, but now with a special focus on data science. I spoke to Kim to find out more about her background in astrophysics and why and how she transitioned to business, and the conversation started when I asked her why she decided to leave academia in the first place.

Kim Nilsson:

It was during my PhD studies when I first started to think hang on, I’m not sure this is the right thing for me because I realised that the things I enjoyed doing the most were actually the project management elements and the making things happen element, rather than the actual technical elements of sitting in front of a computer and coding all day. And so, it started to put a seed of doubt in my mind during my PhD whether it was going to be the right career for me, but I then pursued another two postdoc positions after that because it was a lifelong dream and it’s not easy to let go of that.

I can totally agree with you there. It is really hard. I know many PhD students and postdocs who have exactly that feeling, that they’ve worked so hard and they’ve always wanted to do this and even though they know that they’re potential prospects in academia are limited, they don’t want to leave, and then there’s also the fear of being looked at as a failure when considering options outside of academia. Was that something that ever crossed your mind?

The failure bit – not so much. I think it was more just a fear of the unknown, of taking that jump out of academia, which was the only thing I had ever known, and having really no idea of where I was going to land. I just had to trust myself that I would figure it out somewhere along the way.

So, you made the jump. You left academia after your second postdoc. So, where did you go and what did you do?

I spent about a year applying for just other jobs and jobs within project management, both within science and outside of science, and for other consultancy jobs, management consultancy, strategy consultancy, but I was completely unsuccessful in all of those applications, which of course, really threw me because again, you start to doubt can I really do this. But after that year, I was really bitten by the business bug and I really thought my future is somewhere within business but I’m not quite sure where and therefore I decided to do the MBA and I figured if I have a PhD and an MBA surely someone will want to hire me.

So, do you have any thoughts about why you were so unsuccessful for that year where you were looking for jobs?

I think this is very related to also what we see in the PhDs that we hope to transition into data science today. It’s that when you have spent your life in academia, you are totally unprepared for what business life is like and I mean in terms of communication, in terms of teamwork, in terms of the softer skills, and you have this very academic mindset which many of these companies just do not appreciate, unfortunately, and so it requires a change in mindset and there are many ways to do that but I think I was just too academic in how I came across in those interviews.

So, you went on, you did an MBA – did you enjoy it?

Absolutely, yes, it was a fantastic year.

What often happens to people who do things like an MBA degree is they have a seed of an idea of a business they might like to set up. Is that something that happened to you?

And then about halfway through the programme I met Jason, who is the cofounder of my business, who had a recruitment background before the MBA and together we started to think about all the challenges that me and my friends has faced in making this transition. We started thinking about an industry that is constantly saying that they can’t find enough analytical talent, and we thought there was a gap to be bridged, where we could be really passionate about supporting academics in making that transition.

Transition into…

Initially, it was anything really. We just wanted to help PhDs get jobs. But very quickly after that, we then zoomed in on data science. This was now about seven years ago. It was a new thing. It was just around the time when Harvard said that it was the sexiest job of the twenty-first century, and lots of job opportunities and something that also not very many academics knew about at the time, and so it was an area that we got excited to work in.

It’s funny that they didn’t know about it because there are so many scientists that pretty much all of what they do is data science, especially in a subject like astrophysics.

Very interestingly, in those first couple of years when I would go out to universities and give talks and presentations on careers outside academia, I would ask them what roles they were aware of that they could do and it tended to be finance, it tended to be software development, IP etc., but when I said well, have you heard about data science and this is what the jobs would be like and this is the salary you would get etc., they couldn’t believe their eyes. They were shocked and very, very pleasantly surprised that this option existed, and then they all got very excited about it.

So, what makes a scientist so suitable for working in data science in industry?

I think, especially when you come from a physics background, you will already have done a lot of coding, a lot of software development, so you already have those skills. Secondly, you will already typically have worked with large datasets, with analyses, with maths, statistics, and those are the two largest groupings of skills that you need to be a data scientist. And on top of that, what you then have is this scientific mindset which actually is important in data science because in data science you need to have a hypothesis, you need to set up an experiment, you need to run it, you need to then be able to critically evaluate the results that come out, and all of these are scientific skills. So, in principle, physicists are the full package.

So, what sort of training do you run at Pivigo for scientists who want to become data scientists?

About six years ago now, we started these Science to Data Science programmes (S2DS) and the whole idea was that okay, PhDs, they have these amazing skills already. What they’re lacking is that little bit of commercial polish, as I mentioned, the understanding of how to use these skills within a commercial environment. And so, we built this programme around well, let’s bring together these super smart, super motivated PhDs with companies who want to hire and are interested in data science and get them just to work on a project. So, for five weeks during S2DS, our participants work on a project with a company. They deliver the project as if they were consultants, and they get that experience in a very safe and risk-free environment and it will help them then go out and apply for a job full-time after that.

Now, one of the aspects of your Data to Data Science training programme is that you do some video conferencing so people can do their training programmes from home. Do you find that there are women, particularly with young children, for example, that take part in this because they have young children but they really want to make this transition from science to data science?

Yes, initially our programme was only based in London physically onsite on a campus, but we then decided to start a remote virtual version of the programme, and one of the key reasons for that was because we know there are some people who just can’t travel, who can’t spend five weeks away from home, and so what we see is often the people who do the remote version indeed do have other responsibilities, typically parenting responsibilities. I have a great story, once, about how we were on one of these video calls with a team, discussing the project and it was a very professional conversation. One of the women sat a little bit awkward, but I didn’t think much of it until her husband came up from behind her and picked up the baby that she had been nursing while having this conversation and it blew me away how we are providing an opportunity here for someone who otherwise would not be able to do this, and it was a proud moment both for me and for her.

It sounds like something that I enjoy seeing as well. I’ve been on many panels at conferences and more and more you see women bringing their young children to these conferences, and I’ve even at a few occasions seen women bring their baby up on stage and they’ve had to nurse during a talk, so it’s fantastic that you’re able to offer this opportunity as well. Thank you very much, Kim. So, you’ve actually bought your colleague with you, Deepak. Deepak, can you quickly introduce yourself?

Deepak Mahtani:

My name is Deepak Mahtani. I’m the community manager and data scientist at Pivigo.

So, you’ve actually been through the programme that Kim set up.

Yeah, so I was actually on the virtual programme in March 2016.

And why virtual?

It was the one that came about when I was free. I finished my PhD in January and the next available programme was March, so it was just the right timing.

And why did you decide to transition after your PhD?

Well, towards the end of my PhD, I was thinking about applying for postdocs and so forth, and I applied for one or two, but then the more I spoke to colleagues who were already there, it became very apparent to me that I’d have to move around every three of four years and also, I might have to move country. I might have to move half way across the world and I wasn’t prepared to do that yet. I had a very elderly grandmother at the time and I wanted to start settling down. So, a friend of mine told me about data science and the S2DS boot camp, and the more I looked into it the more fascinated I became and realised that data science really takes all of the bits I loved about my PhD without all of the stuff I didn’t like.

So, what did you do in your PhD?

So, I studied exoplanets, so these are planets around other stars, and specifically I was looking at their atmospheres to try and understand how they work and the chemical and physical properties of them.

And that requires a lot of data processing?

Deepak Mahtani

Yes, so I was very fortunate to use a space-based telescope called Spitzer which gets hundreds of gigabytes of data, and there was just loads sitting in the archive that I was able to analyse, specifically two specific stars, and the time it takes to analyse the data, it’s on the timescale of months. But there’s a lot of it and you gain a lot of really interesting skills from just simple coding to asking the right questions of your data to really critically analysing the results that come out, and those are the key skills that you need for any role specifically within data science.

So, tell me a little bit about the Science to Data Science programme and what is was like for you going through that programme.

Sure, so I came into it with having just about picked up Python and was terrified because I had learnt a very under-utilised language outside of academia, and so this was this brand new programming language and then I was told to build this fancy recommendation engine and I was like oh my god, what do I do now? But one of the best things about the programme is that you’re in teams of three of four people and so you’re able to utilise your strengths and understand where your weaknesses are. And from there, it became really apparent to me that actually through just a bit of googling and trial and error, you can get to where you need to. And just like Kim was mentioning earlier, changing your mindset from that perfectionist mindset of it has to be right first time, to just get it working in a hack and slash way for now, and then once that’s done you can tidy that up and make it faster and more efficient, and that was how we did it. It was good fun.

So, when you completed the five-week course, what happened next?

So, I think we actually finished it on my birthday.

That’s a nice way to finish.

It was, it was really good fun, and then I went on to work at a gambling company and I didn’t really enjoy it very much so I left after about seven months, and about a week before, I’d spoken to Kim, and I was like, ‘Kim, I’m not enjoying myself here.’ So, she said, ‘Come into the office’, so I was like okay. I trundled into the office after work one day and she said, ‘Well, the community manager role has become available and you could do some data science there too.’ And I thought okay, so I went home and I thought about it and I read into the job spec and realised that it combines both my love of technical stuff but also my communication and people skills, and so it was just the perfect job at the right time. So, I applied for it, interviewed for it a week later and had the job on the Friday, so it was a very interesting experience and now I get to travel and talk to loads of PhD students to give them more of the advice that I wish I’d got. I mean I was very lucky that I had someone to talk to and get advice from when I was making my transition, but not everyone has, so I get to go and speak to everyone and give them all of that advice and help them make that transition really smoothly.

So, what sort of advice would you offer to those who are looking to transition?

Well, I try to give tangible advice. So, a lot of times when you look online, it’s just sort of generic do this and do that, but I try to tell them that there’s three main things you should do. You should learn about either Python or R because they’re the two most used programming languages within data science. Read up a little bit on machine learning in that you don’t have to know about every algorithm under the Sun, but understand the differences between, for example, supervised and unsupervised learning and what the difference between classification and regression are. And then understand a little bit of SQL as well because a lot of data is stored in some kind of database and so you really need to be able to access that data and the simplest way to do that is through a relational database which uses SQL. And I also recommend two books that really helped me to understand how to change that mindset from academic to business, which were Crucial Conversations and the other one was called Just Listen , and those two books, what they really do is show you how to be empathetic and understand what your stakeholder is looking for, why they need it and when they need it, and also understanding how to manage those expectations. It’s really important that a lot of stakeholders in the business world might want something tomorrow and you can try and deliver it maybe not tomorrow but the next day, but manage those expectations and those two books really helped me.

Deepak, thank you very much.

You’re welcome.

Now, someone else who’s made the transition from physics to data science is an old university colleague of mine, Lewis Armitage. He completed an undergraduate masters in physics at Cardiff University with me before moving on to do a PhD at Queen Mary University in London. His work was partly based at CERN, the European organisation for nuclear research in Switzerland. Now, he decided, like Deepak, that he needed some more work-life balance and also thought that data science would be where he’d find that. Here’s his story. When you were working on your PhD, you had the opportunity to go out and work at CERN. That must have been super exciting to then be able to go to basically the home of particle physics.

Lewis Armitage:

Yeah, exactly. I mean it was actually so amazing that I didn’t quite believe it myself and I think that actually, my family didn’t really think that I’d ever be able to get there. I can actually remember telling my family that I was actually going to apply for this PhD and I was hoping to get it and they were like, ‘But Lewis, no one works at these institutions. That’s crazy. Only crazily good people work at these institutions.’ And I was like oh, thanks guys, thanks for your confidence. Laughs . But it turns out that physicists from everywhere can work at these institutions because we’ve got really, really good skillsets.

When you got to CERN, what was it like to actually work there?

Well, I was actually quite surprised really because it is an extremely large organisation and then I was working on the ATLAS experiment, which has hundreds and hundreds of people working on it, and you never really meet everyone who works on the experiment. It would be almost impossible to meet everyone.

I find it interesting to think that you’re part of a team where you never actually meet everybody on the team. Did it make you feel like, even though you felt like a superstar having been given the chance to work at a place like CERN, did it make you feel very small?

Yeah, I think it does and I think when you start off, that’s always going to be the impression that you get because everyone there knows a lot of other people there. It’s kind of like your first day of school, you know, you’re there and you’ve got to meet everyone else, you’ve got to make your network. And then everything seems very big in the way that other people already have their analyses to take care of, their own responsibilities, and you’re still kind of finding your feet. But then actually, as the weeks go by, you get more confident and your analysis gets a direction and then you start plugging into these different teams to actually start getting information that you need to move forward. And then towards the end of the PhD, you feel like actually, you know what, I’ve got a place here.

Feeling like the superstar you felt at the beginning when you were accepted. So, what happened next? You decided to make a move into industry.

Yeah, there’s quite a few things that happen in a large organisation such as CERN. One of the, perhaps, downsides is that because there’s a lot of people who work there and there’s a lot of people who are trying to make their name in science, there becomes an element of competition, I think, and it really pushes people to work as hard as they can, and I think that’s really, really good. But it’s got this downside in that you start to give your whole life to the subject. This was something I was noticing really, in that it can be difficult to switch off from the work that you do, from the physics that you’re trying to do, and so, you’ll notice that all of your evenings become occupied and that becomes routine, and then beyond that, all of your weekends are becoming occupied and that’s routine, and I saw this as actually a really unhealthy work-life balance.

So, it wasn’t a lack of love for the subject.

Even though I really enjoyed what I was doing, I couldn’t bring myself to do it every day and to not switch off from it. I really wanted to have my own weekends for myself. I wanted to get back home and just speak to my friends and talk about something completely different.

So, after CERN, you moved to industry. You chose a path of data science. Now, what was the job hunt like?

Yeah, it was quite difficult because I don’t think I really appreciated what you need to do for look for a job. I mean it sounds kind of simple. It comes down to the really basic things like how do you write a CV, how do you write a cover letter, what kind of jobs are interesting, where you should kind of target and position yourself, even how to read a job description is actually really important, and although I had these really strong skills, it was difficult for me to market them properly because I didn’t really know what the businesses were actually looking for and what was actually actionable from my skills, and so that was the thing that I learnt very slowly, actually.

Why do you say it was a slow learning process?

I think being naïve, I think I sent out a load of applications and then I just kind of sat back and thought okay, that’s it, I’ve sent out all these applications, that’s done. And then it’s only when you kind of start only getting a few replies and then they don’t really go anywhere that you actually question yourself and go actually, maybe I’m not as strong as I think I am and then maybe I’ve actually got to review myself and then you modify your CV and your cover letters and the style of it and then you send them out again, and then you get a bit better responses but then it still mostly comes back negative and then you think what it is about this and you can turn to your friends and they can make suggestions for you.

How long did it take you to find a job?

A little less than a year, actually.

What advice should people be following who are interested in a position that is heavy in data science and is in industry?

If you’ve shown that you’ve actually been able to take data and produce results from your data and then interpret that data – and the key thing is interpret – then that would really be the thing that puts you above because physicists have very good critical thinking skills. But then being able to justify that for a data science position, it really depends on the position. It depends if the data science position is actually a half analyst position. If that’s the case then the critical thinking will come in immensely, but if it’s just a data science position that’s more like full stack developer or something like this where the candidate is meant to do the data warehousing, they’re meant to create all these APIs and then also do some data cleaning and data manipulation for some end user or some end result, and it’s really the end user who then looks at the data and decides whether it makes sense or not and then they will feed that information back to the data scientist, If that’s the case then physicists are at a disadvantage there, and that’s really not, in my personal opinion, that’s not the place where physicists should be going because it’s unlikely that you’ve got the data warehousing skills. It’s unlikely you’ve got experience building APIs. I mean maybe you do and that’s good. And so, I think this is a key thing with, again, reading the job description.

So, you are an analyst at Tsquared Insights in Geneva. So, what does an analyst do?

So, for my day-to-day job, essentially, I take data that’s already been processed by an RND team who are full stack developers, and then I have a brief that is the client’s requirements and I’ve got to satisfy those requirements for their analysis and I’ve got to build an analysis around the data. So, I’ve then got to write the code which will then access the database, it will then process the data in a particular way. It will chop up the data into the right components and then it will run various statistical analyses, again depending on what the client wants, and then I’ll output a certain number of files. I take those files and then I put them into some deliverable, whether it’s a presentation or some Excel file perhaps that the client wants. But then there’s a key element there at the very end which is to look at your results and look at the data and to make some insights about it. You’ve got to look at the data and go okay, what’s actionable here? What will the clients find useful? What is going to make us as a business look really good with our data? And then that’s really where I inject my creativity and I inject the critical thinking because that is something that not everyone can do.

Thanks to Lewis Armitage. Now, in the next episode, I speak to Professor Jon Butterworth from University College London and he works at CERN just like Lewis did. He spent many years working on the ATLAS project and supervising students who have done the same. Now, I wanted to speak to him to find out what it’s really like working on such an enormous, international team like ATLAS, which led the discovery of the Higgs boson, especially when there was such a huge media focus around it. Here’s a sneak preview.

Jon Butterworth

One of the nice things with particle physics is it’s not all down to one PI and their lab. There’s a huge number of us, so it was good that wherever anyone in the media pointed their microphone, they found someone who was excited because the excitement was real. But it was also good that, well, some physicists’ worst nightmare is to be in front of the camera and that’s absolutely fair enough. Everyone doesn’t need to do it.

Now, don’t forget you can always find out more about what the Nature Careers team is up to on Facebook and Twitter, and there’s of course the website – www.nature.com/careers. Thanks for listening. I’m Julie Gould.

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The route for a 2nd year physics PhD student to have a career as a data scientist

I am a 2nd year PhD student in physics. Tenure-track positions are highly competitive and I do not love research enough to pursue it as a life career. Since I like programming and playing with data, I want to have a job as a data scientist after finishing my degree. I read some success stories of people who got degrees in Physics but works as data scientists but the people are from top universities like UC Berkely, Stanford, etc... So my question is how doable it is for someone who only gets Physics degree from the low-rank university to find a job as a data scientist. What is the plan for the next years when I am still in my PhD program? What should I learn? How should I have real projects and internships to work on? Will working unpaid in a research lab about data analysis in my current university help?

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• 2 Look at job listings for data scientists. Figure out what they say the requirements are. Then learn those skills. –  Dawn Commented Dec 14, 2017 at 2:09

3 Answers 3

Yes, you absolutely can go from a Physics PhD to a data science career.

The three major routes I've seen have been:

• Apply to a program like the Insight Data Science Fellows (there are many like this), where they take students with strong quantitative backgrounds and build up some of their more industry-relevant skills, then place them in jobs. These can be quite competitive, and my impression is that students who get placed in these fellowships have already done significant work on "side projects" in data science - i.e. you create your own research topic, and find out something interesting. [Also, since they are competitive, I suspect students from high-profile universities have an advantage.]
• Find an internship at a local company; use this to bootstrap your way into industry (or just go work there if you like it!). Again, usually before you get an internship, you usually need to show some interest, working on a more closely data-science-relevant side project, or providing a solution in a Kaggle competition.
• Personal connections. Keep an eye on graduating students now, and see what they do! Many companies need coders with strong quantitative skills, and might offer referral bonuses - someone who graduated a few years before I did reached out to me at one point because of this.

Since you are just starting out, you also have the important option to make your PhD project more closely aligned with interesting data science ideas. It is possible to do both physics and data science - for instance, if I look at the list of sessions at the 2017 APS March Meeting, I see three or four with "machine learning" in the title alone. Of course, this depends on an advisor who is willing to do this and able to teach you relevant things!

However, it is still important to remember that a Physics PhD is a long time commitment, and you have to choose an advisor and a project you will be happy with in the mean time - not just what is going to be popular in industry. (After all, in 3-4 years, the market for data scientists may not be nearly so good.)

If you compete in Kaggle competitions, or the physionet challenge (and win), that will do a lot to prove you are a credible data scientist, no matter what your degree is.

I did exactly this (Physics PhD to data science). I didn't do any 'specific projects' but did some self teaching.

If you want to do help yourself you could learn:

• Brush up on Linear algebra.
• Good knowledge of one high-level programming language (Python, R, etc)
• Awareness of Machine Learning algorithms.

I was already competent in Python and did some basic Machine learning (i.e., regression and basic image classification). I also read the first half of the book:

'Hands-On Machine Learning with Scikit-Learn and TensorFlow' by A. Geron. (I have no affliation to this book or author).

The most important thing is to concentrate on getting your PhD! A good PhD will get you a job in this field rather than the basic understanding you could gain yourself in your free time. I did all my learning while working at a different job for a few months after my PhD.

Following this, I then approached some Data Science jobs and was honest: I have a strong numerate background, but have very little knowledge about data science but want to learn. Several companies were very happy for me to 'train up' because of the potential someone with a PhD has! Particularly, as a physics PhD teaches you great research and problem solving skills.

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How my degree in Physics helped me become a better Data Scientist

I have a Master’s Degree cum laude in Theoretical Physics. When I started my journey into data science, I figured out how useful it is for this kind of job.

My background

I’ve studied Physics at “Sapienza” University Of Rome and attended my Bachelor’s Degree in 2008. Then I started studying for my Master’s Degree in Theoretical Physics, which I obtained in 2010. My focus is the theory of disordered systems and complexity.

Theoretical physics has always been my love since my BS. I hated going to the laboratory and working with lasers and old computers that neither had an updated Windows 98 system able to read USB pen drives (I’m not joking). Instead, I liked programming and software development laboratories. I remember with love a course about Computational Physics in which I learned Monte Carlo simulations and optimization algorithms. Everything was done in pure C language (Python wasn’t as famous as it is now and Matlab was expensive). It was a lot of fun for me. Unfortunately, it wasn’t for most of my colleagues.

Then I studied other programming languages by myself, like R and Python. I remember the transition from Python 2 to Python 3 and how hard it was.

I can say that those 5 years have been stimulating and wonderful. Here are some things I have learned that I used later in my job.

The scientific approach

During my academic studies, I have been trained to have a strong scientific approach to problems. Find the cause and remove it. It’s very common in software programming and in science and it becomes important even in Data Science. Trying to extract information from data is, actually, a very hard problem you have to face backward. You get the problem, then go back to its cause to find a solution. It’s always done this way and Data Science exploits this approach strongly.

Don’t be afraid of approximations

Not all the solutions must be exact. Physics has taught me that approximations are well accepted if you can control them and can give clear reasons for their need. In science, there is never enough time to get the best results possible, so scientists often use approximations in order to publish some partial results while they keep working to better solutions. Data Science is very similar to this way to work. You always have to approximate something (remove that variable, simplify that target and so on). If you look for perfection, you’ll never get anything good, while somebody else will make money with an approximate and quicker solution. Don’t be afraid of approximations. Instead, use them as a storytelling tool. “We start with this approximation and here’s the result, then we move to this other approximation and see what happens”. This is a good way to perform an analysis because approximations can give you a clearer overview of what happens and how to design the next steps. Physics has taught me that approximations are acceptable as long as you can control their error. Remember: you accept the risk of the approximation, so you’ll have to manage it.

The basic statistics tools

In the first year of my BS, I have learned the most common statistical tools to analyze data. Probability distributions, hypothesis tests and standard errors. I’ll never focus enough on the need for the calculation of standard errors. Physicists are hated by anybody because they focus on the errors in the measures and that’s correct because a measure without an error estimate doesn’t give us any information. Anyway, most of the statistical tools I’ve written about in my articles during the years came from the first year of my BS. Only the bootstrap came during the third year and the stochastic processes came during the second year of my MS. Physicists live with data and by analyzing them, so it’s the first thing they teach you. Even theoretical physicist has to analyze data because they work with Monte Carlo simulations, which are simulated experiments. So, Physics has given me the correct statistical tools to analyze each kind of data.

Data is everything

Professional Data Scientists know that data is everything and that algorithms aren’t so important if compared with data quality. When you perform an experiment in a laboratory, data returned by that experiment are the bible and must be respected. You cannot perform any analysis on the noise, instead you must extract signal from the noise and it’s the most difficult task in data analysis. Physics taught me how to respect data and that’s a fundamental skill for a data scientist.

So, here are the reasons why Physics has taught me how to become a better Data Scientist. Of course, Physics is not for everybody and is not a necessary skill, but I think that it can really be helpful for starting this wonderful career.

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From physics to data science

Four physicists share their journeys through academia into industry and offer words of wisdom for those considering making a similar move.

Throughout his higher education, Jamie Antonelli had always envisioned himself as one day becoming a physics professor. All of his role models were professors; all of his peers were working to become professors; all of his research was preparing him for a career as a professor.

“I was living in a bubble,” Antonelli says. “I was keeping my head down and following the same path as everyone around me instead of taking an honest look at my future.”

Every year, a few hundred students like Antonelli graduate with PhDs in particle physics. And every year, only about a dozen permanent positions open up at universities and research institutions. As Antonelli and his peers navigated cycles of applications and rejections, he was hit with a hard truth: Most PhD physicists will leave academia.

Like many of those physicists, Antonelli found his way to a career in data science. It can be a challenging transition to make, especially when students like Antonelli find themselves building a large part of their identity on the idea of staying in basic research or academia.

Symmetry checks in with Antonelli and three other physicists who made the leap to data science.

Jamie Antonelli 

As a junior in high school in 1999, Antonelli watched as his physics teacher dropped a bowling ball and an egg simultaneously, expecting the heavier object to land first. They smashed into the ground at the same time.

“It was like the scales had fallen from my eyes,” he says. “It opened a new realm of understanding that was not accessible by intuition alone.”

From that class forward, Antonelli was hooked. He pursued physics with dogged persistence.

“I wanted to dive as deep as I could,” he says. “No other subject held my interest as much. I wanted to do everything I could to one day become a physics professor.”

As a talented student, Antonelli was a big fish in a small pond. But when he started a particle physics PhD program at the University of Notre Dame and began doing research on the CMS experiment at the Large Hadron Collider, he became acutely aware that he had entered the ocean.

“By the time I got to CERN, I was no longer the best at physics,” he says. “I was surprised how hard it got. The depth of the mathematics pushed me to the limits of my intelligence. It was a great and humbling life experience, understanding where I fit in the world.”

Antonelli pushed through the challenges toward the goal he had set for himself in high school. But as he entered his fifth year as a postdoc, he began to question his choices.

“At the beginning, the competitive culture motivated me,” he says. “It was a blast: working all day and into the evening with all these brilliant people, trying to shine.” 

But in the later stages, he says, he started to see how that same culture was driving him and his colleagues to neglect other parts of their lives. “There was also the subconscious awareness that we were all competing for the same small pool of permanent jobs, and this became a huge source of stress.”

Antonelli says that competition started eating away at the camaraderie within his community of physics friends and coworkers. “I’d watch friends get interviews at places where I had also applied, and it was really difficult to celebrate their achievements,” he says. “Within the academic job market, there are real challenges, real disappointments and real jealousies between friends. It can really bring out the worst in everybody.”

Antonelli started looking for another option, but he had never considered how his skills might apply outside academia. Even thinking about it felt like abandoning a dream.

“Because the field draws people who are motivated and intelligent, it fosters a culture of giving your whole self to your job,” he says. “And I was no exception. I had spent my whole life on this path and had invested so much that I felt like I would be a failure if I went in a different direction. So much of my personal identity was wrapped up in becoming a professor that it was painful for me to give that up.”

Then Antonelli attended a job panel at a physics conference that gave him a new window into the world outside of physics. The moderator for the panel said she found it unconscionable that students in physics were not aware that their peers who had left the field were generally very happy with their work—and making two to three times as much money.

“I had never compared the academic career lifestyle with that outside academia,” Antonelli says. “And it turns out, 70% of the job description for a professor did not interest me at all. It had been my goal for so long that I had never evaluated if it was a good fit for me.”

In 2017 he participated in the Insight Data Science Fellows Program, a seven-week program run by a former member of the ATLAS experiment at the LHC that helps scientists transition from academia to data science. Immediately afterward he found a job in health care.

Antonelli reviews data from hospitals to compare their performance and identify opportunities to improve their quality of care. One of his latest projects involves helping hospitals understand if they are giving equal treatment to different socio-economic groups.

To physics students and postdocs considering making the move to data science, he says, “The world and your career opportunities are so much broader than what they are inside academia. You have highly valued tech skills, and you can take your favorite part of your job and find someone that will pay you to do just that.”

Jennifer Hobbs 

Jennifer Hobbs remembers sitting in science class as an elementary school student, feeling crestfallen. 

“Everything seemed like it had already been done,” she says. “Outside of medicine, it seemed like we really understood everything about how the world worked. I remember thinking, ‘This must be all that exists.’”

But in 1995, when Hobbs was in the third grade, something new happened: Scientists at the US Department of Energy’s Fermi National Accelerator Laboratory discovered a fundamental particle, the top quark. “Here was this real, new science,” she says. “It made me realize that there’s still a lot we can learn about the world.”

Even though, not surprisingly, Hobbs knew nothing about particle physics as a third grader, the top quark discovery stuck with her. She pursued a STEM-heavy program throughout high school and enrolled in an integrated sciences program at Northwestern University. Through Northwestern’s physics department, Hobbs found a way to become part of the laboratory that had captured her imagination so many years before.

“I’d go out to Fermilab every summer and one to two times a week during the school year,” Hobbs says. “I absolutely loved circuits and classical electrodynamics, and these are the skills I used while building detector components for the MINERvA [neutrino] experiment. I felt like I was making a real difference.”

She decided to pursue her PhD at Northwestern so that she could continue working with the same professor on MINERvA, Heidi Schellman. 

But as her graduate school classes started, things felt different. “I can’t really explain it,” she says. “I didn’t have that same passion for physics research that I did for the engineering side of things.”

She kept thinking back to the last time she felt a bubbling excitement for scientific research: during an undergraduate neuroscience class, when a professor had demonstrated how to predict brain activity using Gauss's law—a formula that relates electric charge to electric fields.

“Here were my favorite physics subjects—circuits and electrodynamics—and we were using them in a biology class,” she says. “It totally blew my mind.”

Hobbs says she felt torn between her expectations for herself and where her passions were pulling her. “Physics was something I had enjoyed and thought about since I was a little kid,” she says. “To walk away seemed crazy. What if I choose something I like less? What if I switch labs and then hate it?”

On top of those fears, Hobbs says, she felt like walking away from physics, even to go to a field as challenging as neuroscience, would make her a failure. “There’s this idea that particle physics is the one true hard science,” she says. “As a woman in science, I always felt like I needed to overperform and push myself harder because of an explicit expectation that I would fail. I felt like switching to neuroscience was admitting defeat, like I’m not good enough to keep up with the guys.”

After months weighing her options, Hobbs says she finally came to a realization: “Following my passion doesn’t make me less qualified than someone else. It’s not in anyone else’s court to decide what my passions are and what qualifies as my success.”

Hobbs switched into neuroscience. She examined how touch is processed and encoded by the brain. Her research introduced her to machine learning, giving her the skills to become a data scientist before data science was a well known profession. 

Hobbs says she struggled to communicate her skills to potential employers outside of academia, but eventually she found a position evaluating risks at an insurance company. 

Within a year, she transferred to her current job at STATS, LLC in Chicago, where she uses sports data to analyze player performance and make predictions. “Sports matches are essentially hundreds of controlled experiments that produce all sorts of data,” she says. “We can learn about how people move, make decisions and behave in different situations. As a scientist, this is a dream dataset.”

As a third grader dreaming about fundamental particles, Hobbs could never have guessed where her path would eventually lead her. The advice she gives to others who are considering leaving what they know to try something new is to just go for it.

“It’s OK to feel uncomfortable,” she says. “When you’re too comfortable, you’re not learning as much as you can. Look for opportunities to follow your passion and expand your skillset.”

Dongwook Jang

As a student, Dongwook Jang had a knack for math, but not a clear idea about how he could apply it professionally.

“When I graduated from college with an undergraduate degree, I still didn’t have a picture of a future career,” he says. “I went into a master’s program in physics to give me more time to decide.”

It was during his master’s program at Yonsei University in South Korea that Jang discovered high-energy particle physics. In 1999, he moved to the United States to pursue a physics PhD at Rutgers University.

“I felt protected inside academia,” he says. “I didn’t have a solid plan or know my future, but I had a rough vision of eventually getting a faculty position and doing my own research.”

After completing his PhD and working for five years as a postdoc, Jang found his vision of being a staff scientist had not yet materialized. “There are not many options inside academia,” he says. “The competition was very intense, and I had the realization that I would have to leave the field.”

However, Jang had only a vague idea what his options were. 

“Most of the people I knew who had left academia landed in the financial world,” he says. “During my postdoc, I tried applying to financial companies, but the entrance barrier was very high. They required a deep understanding of computer science, statistics, and a high proficiency in several programming languages. It was like they wanted some kind of superhero.”

Jang was also not a citizen of the United States, a place that was beginning to feel like home. His lack of citizenship or a green card weakened his chances of finding a job in the US.

“I had some friends at CERN who were in a similar situation, but I believe they all went back to South Korea,” he says. Applying for the National Interest Waiver, a waiver that allows an individual to obtain a US green card without the support of a specific employer,  “requires a lot of documents, high fees and time. I had to hire a lawyer who is specialized in this process.”

After two years of effort, during which he continued to conduct his physics research, Jang finally received the green light to work in the US. Jang applied to more than 100 jobs, had 50 phone interviews and 10 onsite interviews. But he felt there was still a mismatch between his qualifications and employers’ expectations.

“Most of the time the interviews were not positive,” he says. “They asked difficult questions about computer science, algorithms, data structure and programming concepts. They were not interested in my physics career, for sure.”

It dawned on Jang that he needed to change his approach. He talked with friends who had left high-energy physics, and one of them told him about an opening in his office. Jang got the job.

“Networking works,” Jang says. “The company already had some employees who came from high-energy physics, and I think they saw how useful we are and that we add value.”

Jang’s transition into industry coincided with the start of the machine-learning boom and presented him with new ways to apply the skills he had cultivated inside academia. Today, he uses machine learning to identify driving patterns and predict future traffic. 

“My work is completely aligned with what I had been doing as a postdoc,” he says. “I’m performing the same kind of data analyses, but instead of using momentum and energy, I’m dealing with location and speed.”

He says he was surprised at the level of challenge and fulfillment that he finds from his work. “I was uncertain when I left physics if I would be happy working in industry,” he says. “But after a few months, I completely changed my mind. There is another life outside academia. And the work-life balance is great.”

Even though Jang has moved away from both his home country and academia, he feels like he’s found a place where he belongs.

“I feel like the United States is my new home now,” he says. “I got married here and have a son who gets his education here. I work here. Where else should I call home?”

Thomas Gadfort 

By all measures of physics success, Thomas Gadfort had made it: In 2012 he made the jump from a postdoctoral position at Brookhaven National Laboratory to an Associate Scientist position at Fermilab. And then, he and his wife decided to have their first child.

“The minute you have a child, things change, whether you want them to or not,” Gadfort says. “I had to do some grand thinking about my life and its direction.”

That direction had seemed clear for most of Gadfort’s life. When he was five years old, his family emigrated from Copenhagen to Oak Ridge, Tennessee, for his dad’s job as a nuclear physicist. “I had posters up in my room of the Standard Model of particle physics before I even knew what it was,” he says. “Physics was a natural home for me.”

Gadfort excelled as a researcher. But as he climbed the physics hierarchy, he saw that his path was pulling him away from his scientific passions. “The next natural step in my career was to lead efforts and manage projects,” he says. “And to be honest, it was not something that I wanted. I just wanted to continue being a postdoc, making plots and trying to understand the details.”

On top of these sentiments, Gadfort started thinking about how he could juggle a successful career in physics and a fulfilling family life. “I wanted to be more of a family man and not work on weekends or travel as much,” he says. “But if I don’t travel, would that make it possible to have the physics career I want, at the level I want?”

After two years working at Fermilab and several months of mulling over his future, Gadfort decided to take a leap of faith and step into the data-science world.

“There was a lot of uncertainty at the beginning,” he says. Four years later, he says, it is clear to him that it was the right decision. “But immediately afterward, I really wasn’t sure.”

As Gadfort started his first job in the private sector, he found that he had the raw abilities of a data scientist—but not the skills. “I didn’t know how to code in Python and had to learn it on the fly,” he says. “Much of my work also involved extracting datasets with unknown and cryptic formats, which was not something that I did as a physicist.”

Gadfort also had to adjust to a structured work culture with deadlines and deliverables. “When I was a postdoc, it was up to me to manage my time,” he says. “If I wanted to spend my day on some random problem or rewriting code, it was not much of problem. Things don’t work like that in industry.”

Another reason the transition was so awkward, he says, is that he wasn’t sure what leaving academia meant to him as a physicist.

“When I was studying physics, I was very proud that I was part of that community,” he says. “I would read about famous physicists and follow all the latest results because the work was meaningful to me.” 

But now that he works outside academia, he still does all of those things. “My fears of losing that part of my identity were completely unfounded. The fact that I’m no longer making plots of Z bosons and top quarks doesn’t really matter. 

“I still think if myself as a physicist, and physics is always going to be one of the loves of my life.”

As Gadfort settled into his new career, he was pleasantly surprised that his work as a data scientist was centered on the same activities that he had excelled at as a postdoc—making plots and analyzing data. He uses data to analyze the behavior of drivers to understand why accidents happen and how to make the roads safer.

Leaving physics also allowed Gadfort to pursue another goal: a healthy work-life balance.

“This past year, I coached my daughter’s soccer team,” he says. “There’s joy and fulfillment outside your career as well.”

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To PhD or not to PhD

Respondents to Symmetry ’s survey about what it’s like to earn a PhD in particle physics or astrophysics offer their views of the experience.

The language of physics

10 more words that mean something different to scientists.

Massless particles can’t be stopped

If a particle has no mass, how can it exist?

What gravitational waves can say about dark matter

Scientists think that, under some circumstances, dark matter could generate powerful enough gravitational waves for equipment like LIGO to detect.

10 words that mean something different to physicists

Some of this science sounds awfully familiar.

popular on symmetry

The boson that physics almost rejected

Nobel Laureate Peter Higgs died earlier this year. Twelve years ago this week, physicists discovered the particle that bears his name.

The deconstructed Standard Model equation

The Standard Model is far more than elementary particles arranged in a table.

Scaling up the dark matter search

Physicists are preparing for the next generation of dark-matter experiments. 

John Bahcall: Godfather of solar neutrinos

Bahcall’s scientific legacy is visible across the field of astrophysics

The data wranglers

A degree in particle physics or astrophysics can lead to a career in data science.

Physics community designs simplified ventilator

The Mechanical Ventilator Milano, or MVM, went from concept through approval by the US Food and Drug Administration in a little over a month.

After a transplant, a physicist takes on heart failure

Physicist Avi Yagil partnered with the doctors who gave him a new heart to bring techniques from particle physics into the evaluation of heart-failure patients.

Transitions into medical physics

Scientists who moved from particle physics or astrophysics to medical physics sit down with Symmetry to talk about life, science and career changes.

Fighting cancer with physics

Researchers at CERN are investigating how very high-energy electrons could help target tumors.

Higgs search alumni: Where are they now?

Meet four physicists who have found different ways to apply the skills they learned through their studies of the Higgs boson.

Conjuring ghost trains for safety

A Fermilab technical specialist recently invented a device that could help alert oncoming trains to large vehicles stuck on the tracks.

Concrete applications for accelerator science

A project called A2D2 will explore new applications for compact linear accelerators.

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Requirements:

A full list of the requirements is also available on the Physics page:

Doctoral students in Physics may submit an Interdisciplinary PhD in Statistics Form between the end of their second semester and penultimate semester in their Physics program. The application must include an endorsement from the student’s advisor, an up-to-date CV, current transcript, and a 1-2 page statement of interest in Statistics and Data Science.

The statement of interest can be based on the student’s thesis proposal for the Physics Department, but it must demonstrate that statistical methods will be used in a substantial way in the proposed research. In their statement, applicants are encouraged to explain how specific statistical techniques would be applied in their research. Applicants should further highlight ways that their proposed research might advance the use of statistics and data science, both in their physics subfield and potentially in other disciplines. If the work is part of a larger collaborative effort, the applicant should focus on their personal contributions.

Grade Requirements:  Students must complete their primary program’s degree requirements along with the IDPS requirements. C, D, F, and O grades are unacceptable. Students should not earn more B grades than A grades, reflected by a PhysSDS GPA of ≥ 4.5. Students may be required to retake subjects graded B or lower, although generally one B grade will be tolerated

PhD Earned on Completion: Physics, Statistics, and Data Science

IDPS/Physics Co-Chairs : Jesse Thaler and Michael Williams

Required Courses:

Courses in this list that satisfy the Physics PhD degree requirements can count for both programs. Other similar or more advanced courses can count towards the “Computation & Statistics” and “Data Analysis” requirements, with permission from the program co-chairs. The IDS.190 requirement may be satisfied instead by IDS.955 Practical Experience in Data, Systems, and Society, if that experience exposes the student to a diverse set of topics in statistics and data science. Making this substitution requires permission from the program co-chairs prior to doing the practical experience.

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The Physics Curriculum Needs More Data Science — and One Team is Making it Easier Than Ever to Integrate It

With support from the aps innovation fund, the dsecop team is getting data science into more undergraduate physics classrooms..

Most physics professors today agree that their students should learn coding and computational thinking — but what about more specialized skills, like those in data science?

The movement to incorporate data science into the undergraduate physics curriculum is gaining momentum, bolstered by a 2021 APS Innovation Fund award to a team who met through the APS Topical Group on Data Science ( GDS ), launched in 2020. The group — Alexis Knaub of the American Association of Physics Teachers, Marilena Longobardi of the University of Basel in Switzerland, William Ratcliff of NIST and the University of Maryland, and Wolfgang Losert of the University of Maryland — saw the Innovation Fund as a chance to respond to a call for help: Physics faculty across the country were asking GDS members to recommend data science textbooks or resources for use in undergraduate classrooms.

“This was something that just needed to be done,” says Ratcliff. “If not us, who?”

After receiving a $200,000 Innovation Fund award for the project, which they called the Data Science Education Community of Practice ( DSECOP ), the team moved quickly. They hired graduate students and postdoctoral researchers as project fellows to develop small data science teaching modules that faculty at any college could easily add to their physics courses. Then, through the GDS, they reached out to colleagues across the country and asked for help piloting the modules with current physics majors.

In addition to the co-PIs, Mohammad Soltanieh-ha of Boston University coordinates the fellows’ activities, Jacob Hale of DePauw University reviews the fellows’ output, and Anil Zenginoglu of the University of Maryland helps manage the community the project has built.

Last June, the DSECOP team organized a multi-day workshop for about 30 graduate students and faculty on the University of Maryland campus, to tinker with and workshop the new modules. Fellow Julie Butler, a doctoral candidate in physics at Michigan State University, says the workshop was a great opportunity to network with other data science-minded people.

So far, Butler has developed two modules for DSECOP, including one on using neural networks, a type of machine learning algorithm. “I show them how to build a neural network from scratch,” she says. The students compare their results with those of a hand calculation and numerical differential equation solver. Beyond just applying a neural network, Butler aims to help students learn how to analyze a problem to determine whether a machine-learning approach would be an appropriate solution, and if so, what type.

In the classroom, “it’s been pretty well received,” says Butler, who has gotten student feedback to improve the module before it’s distributed more widely.

Data science is a comparatively young field, largely enabled by advances in computing power. “I graduated from undergrad in 2006 with a degree in physics, and we weren’t taught data science then,” says Knaub.

But today, physics graduates are taking jobs in industries that are thirsty for opportunities to streamline decision-making processes and innovate quickly. Data science offers a robust way to do this. In industry, “they’re using machine learning as one of the tools in their research toolbox to accelerate the pace of their science,” says Ratcliff.

Despite the demand for physicists with data science skills, the physics community has not responded quickly, says Ratcliff. “Whenever somebody wants to develop new content [for a course], this takes time,” he says.

Knaub adds that “one of the tensions we’re facing with any curricular change is, if we add stuff, does that mean we’re taking things out?” And adding data science content can be a daunting challenge at smaller schools, says Ratcliff, where a department might want to overhaul its curriculum but lacks a data science expert in its ranks.

The DSECOP team has designed the data science modules to readily fill this gap. Each module that Butler has built, for example, constitutes only three Python notebooks, and each can be completed in just one class period. The whole DSECOP project uses Python, because “it’s one of the easier languages to learn.” This should reduce the barrier for faculty adopters, she says.

With the project’s Innovation Fund support ending later this year, the DSECOP team is planning their next steps. “We need to seek additional funding to start really working on the deployment of this material,” says Ratcliff. “It does us no good, even if it's tested material, if it just sits on GitHub.” He says that with additional funding, the team will also be able to accept contributions directly from the physics community.

Ratcliff expects that APS and AAPT meetings, as well as future DSECOP workshops, will play a critical role in getting the DSECOP modules into the hands of physics instructors across the country. Connecting with other faculty “at least gives us the chance to influence or to educate those who are going to be most passionate that these resources exist,” he says. “And that’s often how you start movements.”

Liz Boatman

Liz Boatman is a science writer based in Minnesota.

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Data Science PhD Students

Jaryt Salvo

Expected Graduation Date: 2028-2029

Advisor: Dr. Eric Cooke

Research Topic: TBD

Publications: None

Internships/Job Experience: NASA (2012), YALI (2017), TEA (2018), Oklahoma City Math Camp (2020)

Current Employer: College of Health and Human Services (Center for Justice Research)

Iman Khazrak

Expected Graduation Date: 2028

Advisor: Dr. Shuteng Niu

Research Topic: Transfer Learning, Image generation, Neural Symbolic

Publications: List at Google Scholar

Internships/Job Experience: Data Scientist, First Solar, Summer 2023

Current Employer: BGSU

Aleksei Luchinsky

Expected Graduation Date: Spring 2025

Advisor: Dr. Umar Islambekov

Research Topic: Topology Data Analysis

Publications:

• Kit C Chan, Umar Islambekov, Alexey Luchinsky, Rebecca Sanders, " A computationally efficient framework for vector representation of persistence diagrams", Journal of Machine Learning Research, 2022, 1-33

Internships/Job Experience:

• Adjunct instructor, Calculus/Discrete Math/Business Calculus/Physics/Predictive statistics, BGSU
• Software Developer, Senico Corporation, Bowling Green, Ohio

Current Employer: Student Success Analytics and Technologies at BGSU

Jung Im "Amy" Choi

Expected Graduation Date: December 2024

Advisor: Dr. Qing Tian

Research Topic:  Enhancing Object Detection and Adversarial Robustness through Deep Network Pruning

• Qizhen Lan*, Jung Im Choi*, and Qing Tian, “Aligning Location-aware Discriminants for Structured Pruning of Deep Visual Detectors,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024, under review. (*: equal contributions
• Jung Im Choi and Qing Tian, “Saliency and Location Aware Pruning of Deep Visual Detectors for Autonomous Driving,” Neurocomputing, under review.
• Jung Im Choi and Qing Tian, “Visual-Saliency-Guided Channel Pruning for Deep Visual Detectors in Autonomous Driving,” IEEE Intelligent Vehicles Symposium (IV), 2023, pp. 1-6.
• Jung Im Choi and Qing Tian, “Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios,” IEEE Intelligent Vehicles Symposium (IV), 2022, pp. 1011-1017.
• Jung Im Choi, Jinha Lee, Arthur Yeh, Qizhen Lan, and Hyojung Kang, “Spatial Clustering of Heroin-Related Overdose Incidents: A Case Study in Cincinnati, Ohio,” BMC Public Health, 2022, 22(1):1253.
• Jinha Lee, Jung Im Choi, Arthur Yeh, Qizhen Lan, and Hyojung Kang, “Geospatial Clustering Analysis on Drug Abuse Emergencies,” Hawaii International Conference on System Sciences (HICSS) 2022, pp. 5715-5724.

Ryan Renken

Expected Graduation Date: Spring 2027

Advisor: TBD

Internships/Job Experience: N/A

Current Employer: Graduate Assistant at BGSU

Expected Graduation Date: Summer 2025

Research Topic:  Human-object Interaction Detection; Knowledge Graph

• SKGHOI: Spatial-Semantic Knowledge Graph for Human-Object Interaction Detection(ICDMW2023)

Internships/Job Experience: None

Current Employer: Dr. Niu and Office of Institutional Research at BGSU

Expected Graduation Date: Spring/Summer 2025

Advisor: Dr. Yan Wu

Research Topic:   Transfer Learning for Vulnerability Detection

• Jingyi Su and Yan Wu, Optimizing Pre-trained Language Models for Efficient Vulnerability Detection in Code Snippets, ICCC 23
• Yan Wu, Jingyi Su, David D. Moran and Chris D. Near, Customized Security Triage from Analysis Tools: Current State of the Art, ICCC 23
• Jingyi Su, Shan He and Yan Wu, Features Selection and Prediction for IoT Attacks in High-Confidence Computing Volume 2, Issue 2, June 2022, 100047
• Jingyi Su, Mohd Arafat, Robert Dyer, Using consensus to automatically infer post-conditions, ICSE 18

Current Employer: Heidelberg University, Tiffin, OH

Expected Graduation Date: Spring 2026

Research Topic: Diffusion Model and Transfer Learning

Publications: N/A

Internships/Job Experience: First Solar, Perrysburg, OH

Current Employer: N/A

Department of Computer Science

Dr. Jong Kwan "Jake" Lee, Chair Department of Computer Science Bowling Green State University Bowling Green, OH 43403 [email protected]

Department Office Hayes 221 Bowling Green State University Bowling Green, OH 43403 419-372-2337 [email protected]

The BGSU BS in Computer Science is accredited by the Computing Accreditation Commission of ABET, https://www.abet.org

The BGSU BS in Software Engineering is accredited by the Engineering Accreditation Commission of ABET,  https://www.abet.org

Updated: 06/28/2024 02:06PM

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Graduate studies, commencement 2019.

The Harvard Department of Physics offers students innovative educational and research opportunities with renowned faculty in state-of-the-art facilities, exploring fundamental problems involving physics at all scales. Our primary areas of experimental and theoretical research are atomic and molecular physics, astrophysics and cosmology, biophysics, chemical physics, computational physics, condensed-matter physics, materials science, mathematical physics, particle physics, quantum optics, quantum field theory, quantum information, string theory, and relativity.

Our talented and hardworking students participate in exciting discoveries and cutting-edge inventions such as the ATLAS experiment, which discovered the Higgs boson; building the first 51-cubit quantum computer; measuring entanglement entropy; discovering new phases of matter; and peering into the ‘soft hair’ of black holes.

Our students come from all over the world and from varied educational backgrounds. We are committed to fostering an inclusive environment and attracting the widest possible range of talents.

We have a flexible and highly responsive advising structure for our PhD students that shepherds them through every stage of their education, providing assistance and counseling along the way, helping resolve problems and academic impasses, and making sure that everyone has the most enriching experience possible.The graduate advising team also sponsors alumni talks, panels, and advice sessions to help students along their academic and career paths in physics and beyond, such as “Getting Started in Research,” “Applying to Fellowships,” “Preparing for Qualifying Exams,” “Securing a Post-Doc Position,” and other career events (both academic and industry-related).

We offer many resources, services, and on-site facilities to the physics community, including our electronic instrument design lab and our fabrication machine shop. Our historic Jefferson Laboratory, the first physics laboratory of its kind in the nation and the heart of the physics department, has been redesigned and renovated to facilitate study and collaboration among our students.

Members of the Harvard Physics community participate in initiatives that bring together scientists from institutions across the world and from different fields of inquiry. For example, the Harvard-MIT Center for Ultracold Atoms unites a community of scientists from both institutions to pursue research in the new fields opened up by the creation of ultracold atoms and quantum gases. The Center for Integrated Quantum Materials , a collaboration between Harvard University, Howard University, MIT, and the Museum of Science, Boston, is dedicated to the study of extraordinary new quantum materials that hold promise for transforming signal processing and computation. The Harvard Materials Science and Engineering Center is home to an interdisciplinary group of physicists, chemists, and researchers from the School of Engineering and Applied Sciences working on fundamental questions in materials science and applications such as soft robotics and 3D printing.  The Black Hole Initiative , the first center worldwide to focus on the study of black holes, is an interdisciplinary collaboration between principal investigators from the fields of astronomy, physics, mathematics, and philosophy. The quantitative biology initiative https://quantbio.harvard.edu/  aims to bring together physicists, biologists, engineers, and applied mathematicians to understand life itself. And, most recently, the new program in  Quantum Science and Engineering (QSE) , which lies at the interface of physics, chemistry, and engineering, will admit its first cohort of PhD students in Fall 2022.

We support and encourage interdisciplinary research and simultaneous applications to two departments is permissible. Prospective students may thus wish to apply to the following departments and programs in addition to Physics:

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If you are a prospective graduate student and have questions for us, or if you’re interested in visiting our department, please contact  [email protected] .

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Pacific's MS in Data Science program is designed to equip students with the skills and knowledge required for a successful career in this in-demand field. The program features a flexible, hybrid course delivery tailored to working professionals and traditional students, spanning four semesters and 32 units. As a STEM-designated full-time program, it is CPT/OPT eligible for international students.

A master's degree in data science offers significant benefits for a successful career:

• High demand:  Skilled data professionals are in demand across industries, ensuring diverse opportunities.
• Increased earnings:  Enhance your earning potential as data scientists are among the highest-paid tech professionals.
• Future-proof career:  Stay relevant in a rapidly evolving, data-driven world.
• Professional growth:  Deepen your expertise and accelerate your career with advanced skills in data science.

Pursuing a master's in data science is a wise investment for long-term career success in today's data-driven world.

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Our data science master's program is structured with working professionals and traditional students in mind, offering:

• Hybrid course delivery for flexibility
• Classes conducted outside of regular office hours
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• Industry-sponsored capstone projects for real-world experience

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• Minor in Data Science: Gain sought-after skills and prepare for the MS in Data Science program. For more information, visit the Minor in Data Science webpage or contact DS minor advisor Dr. James Hetrick.
• Dual Degrees in Five Years: Pursue our BS in Economics to Master of Science in Data Science pathway program and earn an undergraduate and graduate degree in just five years to expand your career opportunities.

Admission Requirements

• Bachelor's degree in any field
• Linear algebra
• Programming in a high-level language (experience in R and Python preferred)
• While applicants must have a minimum GPA of 2.65 to be considered for admission, competitive candidates should have a GPA of 3.0 or higher

Application Requirements

Applicants must submit:

• Official transcripts for all college-level coursework
• One (1) letter of recommendation
• Statement of interest 

The statement of interest allows applicants to demonstrate their motivation, skills, and abilities that will contribute to their academic success in our program.

While there is no specific format required for this statement, applicants are advised to give particular consideration to:

• Academic credentials
• Statistics or Linear Algebra
• Computer programming (any language, but Python and R are preferred)
• Commitment and personal stamina to undertake a fast paced, intensive academic program
• Enthusiasm for this particular course of study

Neither the GRE nor the GMAT is required for admission to this program. 

Additional Information for International Applicants

• Must have the U.S. equivalent of a 2.65 GPA to be eligible for admission to the program.
• Must provide an official, course-by-course with GPA transcript evaluation for non-U.S. transcripts from one of the agencies accepted by the University.
• Must provide official English proficiency test scores from either TOEFL or IELTS.

Note: Course-by-course evaluations (and any other documents that are not directly loaded via GradCAS) should be sent to the following address:

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Learn from world-class professors and industry experts.

Seasoned academic scholars and industry professionals teach our data science master's program from leading companies and research centers, including Google, PayPal and Charles Schwab.

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Complete a capstone project that allows you to apply what you've learned and gain hands-on experience working on real projects with prominent Bay Area and Northern California companies.

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The concentration in Physics, administered by the Department of Physics, serves a variety of goals and interests. A concentration in Physics provides a foundation for subsequent professional work in physics, and also for work in computer science, astronomy, biophysics, chemical physics, engineering and applied physics, earth and planetary sciences, geology, astrophysics, and the history and philosophy of science. Less obviously perhaps, the intellectual attitudes in physics — blending imagination, prediction, observation, and deduction — provide an excellent base for subsequent graduate work in professional schools of medicine, education, law, business, and public administration. Students are also eligible to apply for an A.B./A.M. degree program.

Graduate education in physics at Harvard offers students exciting opportunities extending over a diverse range of subjects and departments. In the Department of Physics, graduate students work in state-of-the-art facilities with renowned faculty and accomplished postdoctoral fellows. The department’s primary areas of experimental and theoretical research include atomic and molecular physics, quantum optics, condensed-matter physics, computational physics, the physics of solids and fluids, biophysics, astrophysics, statistical mechanics, mathematical physics, high-energy particle physics, quantum field theory, string theory, and relativity.

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Computer Science (PhD)

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The doctor of philosophy in computer science is, above all, a high-quality degree that is not conferred simply for the successful completion of a specified number of courses or years of study. It is a degree reserved for students who demonstrate a comprehensive understanding of computer science and an ability to do creative research. Each PhD student produces a significant piece of original research, presented in a written dissertation and defended in an oral examination.

The PhD program is structured to facilitate the process of learning how to do research. Students begin by taking required courses to build a foundation of knowledge that is essential for advanced research. Early in the program the student gains research experience by undertaking a directed research project under the close supervision of a faculty member and the scrutiny of a faculty committee. In the later stages of the program, students take fewer courses and spend most of their time exploring their dissertation area to learn how to identify and solve open problems. The final steps are to propose an independent research project, do the research, and write and defend a dissertation.

Application materials should be submitted by December 15 for the following fall term. Materials include everything required for admission to the master’s program as well as a discussion of the anticipated research area.

Students who enter the UO with a master’s degree may petition the Graduate Education Committee for credit toward the course requirements listed below, indicating how their prior graduate work corresponds to these courses. See the graduate coordinator for the petition.

Program Learning Outcomes

Upon successful completion of this program, students will be able to:

• Core Knowledge Breadth: Demonstrate a broad working knowledge of fundamental theories, research findings and methodological approaches in multiple content areas within Computer Science (Foundations, Systems, Data Science).
• Core Knowledge Depth: Demonstrate a deep working knowledge of advanced theories, research findings, and methodological approaches within one of the Computer Science areas of Foundations, Systems, and Data Science.
• Software Engineering: Demonstrate a working knowledge of software engineering and development techniques and related hands-on skills.
• Scientific Inquiry: Achieve a deep fluency in the scientific literature and the ability to ask and pursue compelling questions within a primary field of research, and achieve proficiency in relevant experimental design, methodology, and data analysis/statistical methods.
• Scientific Communication: Demonstrate effective oral and written scientific communication skills.

PhD Course Requirements

A grade of B- or better is required

Cannot duplicate Depth course used

Cannot duplicate Breadth course used

A grade of C or better is required in graded elective credits

PhD Degree Requirements

PhD candidates who enter the program without a master’s degree in computer science must take 48 credits in graduate course work including the core and cluster courses required for the MS program. Doctoral students must earn a minimum grade of B– and an overall GPA of 3.50 in the six courses they use to satisfy the breadth and depth requirements.

Minimum Annual Enrollment

PhD students are expected to enroll in at least 6 credits of 600-level course work each year until their advancement to candidacy. Research: [Topic] (CS 601), Dissertation (CS 603), and Reading Conference: [Topic] (CS 605) do not satisfy this requirement. After candidacy, PhD students are encouraged to continue participation in 600-level courses

Directed Research Project

Complete a directed research project, which is supervised by a faculty member and evaluated by a faculty committee. The research project comprises the following:

• The definition and expected results of the project in the form of a Directed Research Project Contract
• Delivery of the materials constituting the results of the project and oral presentation of the results
• A private oral examination by the committee members

Status Change

PhD candidates are admitted conditionally. Successful completion of the directed research project leads to a change in the student’s doctoral status from conditional to unconditional.

Dissertation Advisory Committee

After successfully completing the directed research project, PhD students form a Dissertation Advisory Committee chaired by their research advisor. The main role of the committee is to advise the student between completion of the research project and mounting the dissertation defense. The committee takes primary responsibility for evaluating student progress. In addition, it approves the plan for the area examination, which in turn is approved by the graduate education committee. See the graduate coordinator for further instructions.

Area Examination

The student chooses an area of research and works closely with an advisor to learn the area in depth by surveying the current research and learning research methods, significant achievements, and how to pose and solve problems. The student gradually assumes a more independent role and prepares for the area examination, which tests depth of knowledge in the research area. The examination contains the following:

• A survey of the area in the form of a position paper and an annotated bibliography
• A public presentation of the position paper
• A private oral examination by committee members

Advancement to Candidacy

After the area examination, the committee decides whether the student is ready for independent research work; if so, the student is advanced to candidacy.

Dissertation and Defense

Identify a significant unsolved research problem and submit a written dissertation proposal to the dissertation committee. The dissertation committee, comprising three department members and one member from an outside department, is approved by the graduate education committee. In addition to these four, the dissertation committee often includes a fifth examiner. This outside examiner should be a leading researcher in the candidate’s field who is not at the University of Oregon. The outside member should be selected a year before the candidate’s dissertation defense, and no later than six months before.

The student submits a written dissertation proposal to the committee for approval, and the proposal is then submitted to the graduate education committee. The proposal presents the research problems to be tackled, related research, methodology, anticipated results, and work plan. The committee may request an oral presentation, similar to the area exam, which allows the student to explain and answer question about the proposed research. The student then carries out the research.

The final stage is writing a dissertation and defending it in a public forum by presenting the research and answering questions about the methods and results. The dissertation committee may accept the dissertation, request small changes, or require the student to make substantial changes and schedule another defense

Division of Graduate Studies Requirements

PhD students must meet the requirements set by the Division of Graduate Studies as listed in that section of this catalog

Research Areas

It is important that a PhD student be able to work effectively with at least one dissertation advisor. Hence the student should identify, at an early stage, one or more areas of research to pursue. The student should also find a faculty member with similar interests to supervise the dissertation.

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Thinking about changing into Data Science as a Physics Student

So a little background, I'm currently working on my second bachelor's degree in Physics. My first degree is in Public Administration, but I found that I didn't like it, and decided to go the STEM route because I loved physics and mathematics. Physics will always be my passion. However, I'm almost 30 years old now, and to do what I really want to do in physics, I need to get a PhD, which means I'll have to finish this bachelor's, get a masters, get a PhD, then do three years of postdoc research, and THEN get a degree in my field. Lately, I've been considering my university's Masters of Data Science program. I've always loved computer science, and I've always been quite good at it. I've taken several python courses (I know that isn't enough to get a job, but it proves that I'm good at it and enjoy it), and I'm looking at changing into the Data Science Master's program here. I feel like I would spend a lot less time in school, be able to jump into a career much faster and establish myself in the field, and have a lot more job opportunities than I would with a PhD in physics. Do you guys have any advice, or has anyone been in a similar situation as I have been? Any and all advice is appreciated!

Machine Learning & Data Science Foundations

Online Graduate Certificate

Apply to Expand Your Future

As the value of data continues to skyrocket, companies are in need of people who can transform large data sets into rich analytical insights. Now, you can learn these techniques in Carnegie Mellon’s cutting-edge online program. Apply today to expand your future in machine learning and data science.  

Are we the right fit?  

Let’s face it, pursuing any kind of advanced training is an investment of your time, energy and resources. Before you consider our program, make sure your background aligns with our program expectations.  

Successful applicants will have:

• A bachelor’s degree in STEM or related field  Successful applicants will hold a degree in a science, technology, engineering or math-related field. Other degrees will be considered if the applicant can show the necessary proficiency in math and programming.  
• Proficiency in advanced math  Students should provide evidence of successful completion of advanced math coursework such as calculus, linear algebra and statistics.  
• Proficiency in programming  Students should be proficient in Python, R, or an analogous programming language, with experience writing at least 1000 lines of code.  
• Relevant work experience   Ideally, applicants will have some relevant work experience in either computer programming or a related field. Internships or other related work are acceptable.  
• A disciplined and motivated mindset  Harder to measure, but equally important, successful applicants will have a resilient spirit, a hunger to learn, and a knack for solving problems through technical innovation. With courses taught by CMU faculty from the #4 computer science school in the country, a consistent and conscious effort will be required to master each topic.

If you have questions about the program or how it aligns with your background, please call 412-501-2686 or send an email to  [email protected]  with your inquiries .

Application Requirements

Ready to apply? Here’s what you’ll need to complete the admissions process: 

✔ Complete the online application Submit your application in the application portal.

✔ Submit your resume/CV We’d like to learn more about your employment history, academic background, technical skills, and professional achievements. Submit a 1 to 2 page resume or CV showcasing your experience. 

✔ Submit your transcripts Submit an unofficial copy of your transcript for each school you attended. Transcripts must include your name, the name of the college or university, the degree awarded (along with the conferral date), as well as the grade earned for each course. Email your transcripts directly to [email protected] .  

✔  Upload a statement of purpose Tell us your professional story. Where have you been, and where do you hope to go? In 500 words or less, please share how our program would advance your capabilities in your current role or prepare you for a new role in the industry. 

✔ Submit your TOEFL, IELTS, or DuoLingo test scores An official TOEFL, IELTS, or DuoLingo test is required for non-native English speakers. This requirement will be waived, however, for applicants who either completed an in-residence bachelor’s, master’s, or doctoral degree program in the United Kingdom, United States, or Canada (excluding Quebec) or have at least three years of professional work experience using English as their primary language. If you fall into one of these categories, please include this information on your resume.  

Tuition: Invest in Your Future

By enrolling in our graduate-level program, you'll be investing in your professional growth to expand your skillset or advance your career. We know this is a significant investment. Not just for you, but for your family as well.

Scholarships To help offset the cost of tuition, and to make our program as accessible as possible, we offer a limited number of partial, merit-based scholarships. All applications will be evaluated for these awards automatically; there is no need to submit additional materials. If you are awarded a scholarship, you will be notified in your decision letter.  All applicants who submit by the priority deadline will receive a partial scholarship award.

In addition, Carnegie Mellon alumni are eligible for a scholarship to the Graduate Certificate in Machine Learning & Data Science Foundations worth up to 20% of tuition. Indicate your alumni status within the application to be eligible.

So, what is the investment per course? Below is a breakdown of our tuition for the 2024/2025 academic year:

• An additional technology fee of approximately $230 will be assessed each semester.
• The rates above are for the 2024/2025 academic year only. If the program is not completed within that time frame, tuition may increase slightly for the following academic year.

Financing Your CMU Graduate Certificate

Monthly payment plan.

CMU provides a monthly payment option , managed by Nelnet Campus Commerce, designed to help students spread out tuition payments into manageable monthly installments. This plan also offers the ease of online enrollment. Should you be admitted and choose to join us, we recommend registering for this plan early to fully benefit from the range of payment options available.

Financial Aid & Private Loans

Students pursuing a graduate certificate are not eligible to receive federal financial aid. However, private loans are a viable alternative to consider with competitive interest rates and borrower benefits. See FastChoice , a free loan comparison service to easily research options.

Employer Tuition Reimbursement

Many companies offer tuition reimbursement programs to foster professional development among their employees. We encourage you to contact your HR department to find out if similar opportunities exist at your workplace. 

When you speak to your employer, you can share that our program: 

• Consists of transcripted, credit-bearing courses (not just continuing education units). You will earn 36 Carnegie Mellon graduate-level credits when you complete the full program.  
• Equips you with foundational skills in AI, machine learning, and computational data science, which means you’ll be ready to extract meaningful insights from large, complex data sets right from the get-go. With the #1 program in Artificial Intelligence and the #1 Programming Languages school in the country, CMU is the ideal place to learn these skills and techniques.
• Features coursework taught by CMU faculty experts who are spearheading research in language technologies, computer science, machine learning, and human-computer interaction. 
• Is delivered completely online , which means you can take classes on your own time while maintaining your normal work schedule.

Not sure how to approach your employer? Need specific documents to proceed with enrollment?  Call 412-501-2686 or send an email to  [email protected]  with your inquiries .  We’re here to help you take the next step in your professional  journey. 

CMU EMPLOYEE TUITION REIMBURSEMENT

The Graduate Certificate in Machine Learning & Data Science Foundations is eligible for CMU tuition remission. Review the   CMU tuition remission policy   to check your eligibility.

A Note for International Applicants

As part of a global university with locations and students from around the world, the School of Computer Science welcomes the diverse perspectives that international students bring to our programs.

The Graduate Certificate in Machine Learning & Data Science Foundations provides a unique opportunity for individuals nearly everywhere to earn a certificate at the intersection of AI, machine learning, and computational data science from one of the top ranked computer science schools in the country. 

To help ensure you are fully prepared for the admissions process and, if admitted, for success as a student, this section provides detailed information about requirements for international applicants.

We look forward to reviewing your application.

The Graduate Certificate in Machine Learning & Data Science Foundations considers for admission international applicants who reside within, or outside of, the domestic United States. International applicants who reside within or outside of the domestic United States are advised of the following information and additional requirements for international applicants to the program.

Student Visas

Since this program is fully online, enrollment in this program will not qualify students for any type of visa to enter or remain in the United States for any purpose. 

Time and Attendance Requirement  

Classes for the program will be taught on the U.S. Eastern Time zone schedule, and students must be available to attend all live classes, regardless of location.

U.S. Sanctions; U.S. Sanctioned Countries

Individuals who are the target of U.S. sanctions or who are ordinarily resident in a U.S. sanctioned country or who live or expect to live in a U.S. sanctioned country while participating in the program are not eligible for admission to this program due to legal restrictions/prohibitions and should not apply. U.S sanctioned countries are currently Belarus, Cuba, Iran, North Korea, Russia, Syria and the following regions of Ukraine: Crimea, Donetsk and Luhansk. In addition, all or a portion of this program may not be available to individuals who are ordinarily resident of certain countries due to legal restrictions.  

Applications received from these individuals will not be accepted. As well, if an individual is admitted to the program and subsequently the individual becomes the target of U.S. sanctions, ordinarily resident of a U.S. sanctioned country or lives in a U.S. sanctioned country while participating in the program (or otherwise becomes ordinarily resident of country in which the program is not available due to legal restrictions), the individual’s continued enrollment in the program may be terminated and/or restricted (due to U.S. legal restrictions/prohibitions) and the individual may not be able to complete the program.  

Licensure in Various Jurisdictions

From time to time Carnegie Mellon reviews the licensing requirements of various jurisdictions in order to assess whether Carnegie Mellon may be precluded from making the program available to applicants that are residents of one or more of these jurisdictions prior to Carnegie Mellon obtaining the relevant license(s). Affected applicants from these jurisdictions, if any, will be notified prior to enrollment if Carnegie Mellon determines that it is unable to make the program available to them for this reason.

Value Added Tax (VAT) and Other Taxes

The tuition, required fees and other amounts quoted for this program do not include charges for applicable Taxes (hereinafter defined). The student is responsible for payment of all applicable Taxes (if any) relating to the tuition, required fees and other amounts required to be paid to Carnegie Mellon for the program, including any Taxes payable as a result of the student’s payment of such Taxes. 

Further, the student must timely make all payments due to Carnegie Mellon without deduction for Taxes, unless the deduction is required by law. If the student is required under applicable law to withhold Taxes from any payment due to Carnegie Mellon, the student is responsible for timely (i) paying to Carnegie Mellon such additional amounts as are necessary so that Carnegie Mellon receives the full amount that it would have received absent such withholding, and (ii) providing to Carnegie Mellon all documentation, if any, necessary to permit the student and/or Carnegie Mellon to claim the application of available tax treaty benefits (for Carnegie Mellon review and completion, if warranted and acceptable). 

Taxes mean any taxes, governmental charges, duties, or similar additions or deductions of any kind, including all use, income, goods and services, value added, excise and withholding taxes assessed by or payable in the student’s country of residence and/or country of payment (but does not include any U.S. federal, state or local taxes).

• What kind of academic background do I need? Successful applicants will have a bachelor’s degree in a STEM-related field. Other degrees will be considered if the applicant can show the necessary proficiency in math and programming. Applicants should also have proficiency in programming languages like Python or R, with experience writing up to 1000 lines of code. 
• Do I need work experience? Applicants will ideally have some relevant work experience in either computer programming or a related field. Internships or other related work are also acceptable.
• What materials do I need to submit when I apply to this program? Besides the online application, applicants must submit a current resume, transcripts, and a personal statement to be considered for enrollment.
• Is there an application fee? No, this program does not require an application fee.
• When is the application deadline?  All applicants who submit by the priority deadline of July 9, 2024 will receive a partial scholarship award. The final deadline to apply is July 30, 2024.
• How do I check the status of my application? You can view the status of your application at any time in the application portal. A decision letter from Carnegie Mellon will be sent through the application portal within a few weeks of submitting your online application.
• After I submit my application, when will I hear back? You’ll receive a decision letter within a few weeks of submitting your application.
• Is a deposit required to secure my spot? No, a deposit is not required to secure your spot in the program.
• If I choose to complete the entire certificate, what is my total investment? The total investment for the Machine Learning & Data Science Foundations certificate during the 2024/2025 academic year is $25,452. A breakdown of the tuition and fees can be found above. Partial scholarships are available. All applicants who submit by the priority deadline of July 9, 2024 will receive a partial scholarship award. Carnegie Mellon alumni are eligible for a scholarship to the Graduate Certificate in Machine Learning & Data Science Foundations worth up to 20% of tuition.
• Is this program eligible for CMU tuition remision? Yes, the Graduate Certificate in Machine Learning & Data Science Foundations is eligible for CMU tuition remission. Review the   CMU tuition remission policy   to check your eligibility.

Application Deadlines

Priority*: July 9, 2024 Final: July 30, 2024

*All applicants who submit by the priority deadline will receive a partial scholarship award.

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected] with your inquiries.

Fast Admission Decisions

Applications are evaluated on a bi-weekly basis, which means you’ll receive a decision letter fast,  within a few weeks  of submitting your application .  

At CMU, we recognize the value of time well spent. Quick decisions mean less time wasted and more time preparing for your future.

Due to the individual nature of the coursework, space is limited for our program - applications will be accepted until the class is full.

Data Science

Data-efficient deep learning using physics-informed neural networks.

A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviours expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations.

Dr. Maziar Raissi