physics phd to data scientist

From PhD to Data Scientist: 5 Tips for Making the Transition

Originally posted by Douglas Mason with Valerie Bisharat

Douglas Mason, Harvard Physics PhD, Insight Fellow, and Data Scientist at Twitter, outlines his advice on transitioning from academia to data science.

About a year ago, I began my unexpected but rewarding transition to industry after completing my physics PhD. My dream for years before that had been to work as a physicist in the National Laboratories, but when the time came to do so, that fate just didn’t feel right.

Before Insight, I had virtually zero knowledge of how to score a job at a tech company like Twitter, Facebook or Google. Instead, what I carried with me before, during, and after the program was a relentless enthusiasm. I’m certain that was key to my success, leading to my current role as a data scientist at Twitter.

Here are my top tips on transitioning from academia to the tech industry:

1. Show that you want it. I’m now part of the interview process to select new hires at Twitter. You wouldn’t believe how many people come through here saying, “Well, the academic thing just isn’t working out for me. I guess I’ll do this now.” We can tell on your resume and in the interview if you didn’t put in any effort to look impressive. If that’s the case, what’s going to happen when we hire you? We want people who actively want to be here.

2. Emphasize the parallels between your thesis work and potential professional projects. My data science career is hugely impacted by having written a thesis. Remember that as a PhD you’ve essentially conducted a five-year project that you were totally responsible for. You’re also constantly giving talks, presentations and summaries of your research, which is exactly what my job involves as a data scientist. Work projects are essentially like an entire thesis compressed into one or two quarters. Only now, you have a bunch of colleagues who will help you figure the problems out. In your resume and the interviews, find ways to draw parallels between your work experience from your PhD and the responsibilities outlined in the job description.

3. Take time to practice your hard skills. They aren’t easy.

• Study your algorithms and data structures from a lot of different sources.
• Give yourself time to learn recursive programming — it’s a different way of thinking, so you can’t do it in a night.
• Review your statistics . Know your different regression types , as well as p values and t-tests .
• Know how to calculate expectation values in combinatorics problems.
• Learn and work with SQL .
• If you’re using Matlab, Fortran, etc. it’s time to make the transition to Python or R . Build fun side projects to practice your skills.

4. Interact with the tech community as much as possible. Learn the language, the lingo, how people talk, how people think. Learn what people value. If you go in having done none of this, you will sound like an alien. You have to show, by doing , that you’re willing to learn the vernacular.

5. Choose a company with an employee size that fits your needs. I love that Twitter is a medium sized company — it’s big enough that I can learn a lot from the experts around me, but small enough that I can have a big impact. For me, that’s an ideal balance. Consider your desired mix, and get lots of advice from veterans along the way.

Remember, this process will throw lots of unknowns at you. In fact, the unknown is the only constant on this path. Get comfortable with that, stay focused, be positive and work hard. Going out on a limb is usually worth it.

Interested in transitioning to a career in data engineering? Find out more about the Insight Data Engineering Fellows Program in New York and Silicon Valley, apply today, or sign up for program updates.

Already a data scientist or engineer? Find out more about our Advanced Workshops for Data Professionals. Register for two-day workshops in Apache Spark and Data Visualization , or sign up for workshop updates.

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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.

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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.

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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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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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How to Enter Data Science With Physics Background

When choosing a career option, everything feels 10x times harder and more confusing. You've just completed your physics degree, and now you're surfing through the internet, reading articles about making a career. Plus, you've already asked all your friends and family, everything you can. 

What if we told you there was a third, much easier option? You'd jump on it instantly, wouldn't you? Introducing the spectacular option: data science! 

Skeptical? Well, sit back and relax! We're here to help you move into data science with your physics background. 

What Does Data Science Refer To?

Before diving deep into how you can transition from physics to data science, let's clear out the basic definition. What is data science itself, you ask? Allow us to explain!

Data science means combining different fields of work in statistics and computing. This is done to interrupt an array of different data, mostly for decision-making purposes. Plus, you need to have a basis for programming language.

How Do You Get Into Data Science With A Physics Background

Sure, you know what data science refers to, but do you know how to transition? No? Worry, not! That's exactly what we're going to help you with. Ready? Let's get started.

1.     Let's Talk Compatibility 

Before you start writing up the 'perfect CV' and practice for your interview, let's talk about how well you can do.

A lot of the time, when you bring up data science, people think 'computer science degree.' But this is because these two things correlate quite easily. To make it easier for you to transition, let's talk about the similarities between these two fields. 

• You learn to carefully collect data 
• Next, both fields require analyzing the collected data
• You build models and sheets to explain what data you have and predict the future
• Lastly, you present your conclusions to your superiors

2.     Don't Be A Jack-Of-All-Trades, Choose Your Domain

It's no secret: physicists are good at experiments and data analysis. But here's the thing, physicists work in all fields included in data science, i.e., electrical engineering, mechanical engineering, data science, mathematics, and somewhat statistics. That, however, does not mean you excel at any of these.

Instead of trying to balance a hundred skills together where you're only moderate at each, choose one skill and work on it. 

3.     Practice Coding 

Now that you've chosen what skill set you're going to showcase; it's time to pay attention to domain knowledge. 

In order to excel or simply work as a data scientist, you need to know a little bit about programming. As a beginner, coding can be confusing. So, start with Python or R, both of which are relatively easy, and then later move onto the harder ones. 

4.     Use Your Skills to Awe Others

Now that you've done both of the above steps, it's time to showcase your skills. Here's a couple of things to keep in mind when entering the world of data science:

• Know your career path
• Practice coding
• Learn programming language
• Practice on different websites and work on some projects

This gives you both the knowledge and skillset you need to step into the world of data science! 

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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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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?

• career-path
• changing-fields
• early-career

• 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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Physicist Turned Data Scientist I: A Path from Academia to Industry

Director Data Scientist, Mastercard

by Saeed Mirshekari, PhD source:  www.saeedmirshekari.com

Table of contents:

Who am I and to whom I am writing this note? Why am I writing this note? From Academia to Industry What is Data Science and who is a Data Scientist? What Skill Set Is Needed? [learning resources] 3 Steps Between you and Becoming a Data Scientist About The Author

Who am I and to whom I am writing this note?

I am a Data Scientist with background in Physics. I write this note to anyone with academic background who is interested to become a data scientist in industrial sector. Although this note targets the academics with advanced degreed in quantitative fields such as Physics, Statistics, Math, and Engineering, interested people from other relevant backgrounds may also find reading this note useful.

Why am I writing this note?

I believe the information on how one can pursue their interests has to be accessible to all globally. The differences in the level of success should not be because of the lack of information, in my opinion. However, it can be because of the differences in creativity, strategy, and hard work, for instance.

Although it is obvious that there is not only one way to success, it is not always obvious how a successful way would look like. It has been my own question. I have also received similar questions from many fellows and juniors asking about the same issues frequently. Since I could not find a comprehensive, instructional note on the path from academia to data science in industry, I decided to write this note and share it with public.

From Academia to Industry

Back in January 2016 a fellow physicist friend of mine who was a postdoc researcher in Europe at the time asked me about my new job as a Data Scientist and how I like it so far. He mentioned that he is also interested in doing data science and wants to know more about it. He essentially wanted to know where he should start from to become a data scientist. Specially, he wanted to know what skill set he would need to do so, and where he has to search for a data science job. He was not the first one asking these questions from me since I landed to the field of Data Science. I asked him to let me respond to these questions in details later this weekend when I get some more free time.

Responding to my friend's questions, here in this note I would like to briefly picture the path I, myself, went through over the past 18 months to switch from a science postdoctoral research position in academia to a data scientist role in tech industry. In addition to responding to my friend's questions, I hope this article would be also useful for other people with advanced degrees in science and engineering out there who want to choose data science as their future career and may have similar questions.

As a former postdoc and having met many fellow postdocs during several years in academia, I believe that in a successful career a postdoc position is an excellent place for transition (either to a permanent faculty job in academia or to a proper job in industry section) but not necessarily the best place to stay for a long time. Not so many years ago almost all science PhD/postdocs were intended to be the future university professors after one or two postdocs. This intention has changed quite a lot over the past decade due to several reasons including the lack of proper faculty jobs in academia from one side, and the highly growing job demand for science PhDs in industrial section on the other side.

Therefore, because of various reasons (to see some of them click here) many PhDs in science, engineering and even in other fields may find that academia is not the best place for them to settle and therefore eventually decide to leave. In fact only a very small percentage of PhDs will remain in academia for their entire career i.e. professors [for example, see above diagram from this report published in UK, 2010].

Having academic background in science and technology, one justified path would be landing in the fast growing field of data science. The rest of this note would be more useful for this group of people who has just started to think about taking this path to become a data scientist. They might want to know more about what it is, how they can get prepared for that, and where they have to look for the best opportunities. I will try to address these questions in the next sections.

What is Data Science and who is a Data Scientist?

In reality, because of the nature of this field, there is no unique definition for data science and data scientists. The definitions and descriptions can vary in a huge range. But here is my version:

A data scientist is a skilled professional with scientific mindset who uses the past and current data to ask [and eventually answer] the right questions in order to make the most informed future decisions in an organization.

There are many popular quotes from the seniors of the field to define data science. Each of them describes one aspect quite well from one perspective and they all together could give you a good description of the truth about data science and data scientists. Here are the quotes that I like the most on the importance/definition of data/data-science/data-scientists.

Data is the new oil? No: Data is the new soil. — David Mccandleuss

Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others. — Mike Loukides, VP, O’Reilly Media.

Data scientist is a person who is better at statistics than any software engineer AND better at software engineering than any statistician. — Josh Wills

By 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge. — Game changers: Five opportunities for US growth and renewal, McKinsey report.

This hot new field [data science] promises to revolutionize industries from business to government, health care to academia. — The New York Times

The data scientist must have knowledge in applied science, with an extensive experience in its industry, and training in science. — Juan F. Cía, BBVA

And here is my most favorite joke on the definition of data scientist retweeted by Chris Dixon “A Data Scientist is a statistician who lives in San Francisco!” which also delivers some part of the truth.

To have a broader view of the field of data science and big data, I highly encourage you watching the 2-minutes video message of the former US President Barack Obama on Data Science  in 2015 introducing DJ Patil, the first official Chief Data Scientist of White House following by a remarkable 10-minutes talk by DJ Patil and also a fun 12-minutes public talk by Hillary Mason in 2012.

What Skill Set Is Needed? [learning resources]

Generally speaking, the skills one would need to get a data scientist job usually fall into three categories [see the diagram]:

• Math and statistics
• Programming and hacking skills
• Domain knowledge

Depending on your background in academia, you may have to focus on improving your skills in different categories from the above list. As a physicist with advanced academic degrees, you should have no serious problem in math and statistics part. However, the other two categories i.e. domain knowledge (substantive expertise) and hacking skills (machine learning algorithms, programming skills, advanced software tools, etc) are those you have to focus on more instead. A detailed and relatively complete list of skill sets needed for a data scientist is available in this diagram. Talking about the most important skills for data scientists, I also found this article very informative.

There are so many subjects and tools under the hacking skills category which are useful to get yourself familiarized with during the preparation phase. However, nobody knows everything about all of them and that’s totally fine. Among all, the most important computer and hacking skills which are under high demand, in my point of view, are the following.

• Machine learning algorithms: Regression, Classification, Clustering, Recommender Systems, Neural Networks, etc.
• Programming Languages: Python, R, SAS, etc.
• Big Data tools: Map Reduce Fundamentals, Hadoop, Spark, SQL, etc.
• Visualization tools: D3.js, ggplot2, matplotlib, etc.

I personally got interested in Data Science when I was a research postdoctoral fellow in data analysis group of LIGO. As part of this job, I had this opportunity to learn and work with several tools and techniques in data analysis of big data from science observatories of gravitational waves which helped me a lot along the way. I started my first Data Science projects when I took an online 10-weeks course on Machine Learning by Andrew Ng of Stanford. I totally enjoyed the lectures and homework projects such that I took more courses on the field afterwards and started to think more seriously about choosing data science as my career path. Following is a schematic of my career path towards data science which is only one way out of many other possible ways approaching data science.

Among all the useful, available textbooks on applied statistics and machine learning algorithms, essential for data science, I recommend the following books. They are all available online by their publishers for free:

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction  by Trevor Hastie, Robert Tibshirani, Jerome Friedman. (see the course: Statistical Learning on Coursera by the same group of authors)
• An Introduction to Statistical Learning with Applications in R  by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• Mining of Massive Datasets  by Jure Leskovec, Anand Rajaraman, Jeff Ullman. (see the course: Mining of Massive Data offered by Jure Leskovec)
• There are a growing number of weblogs active writing about data science on a regular basis. Some of the most interesting ones to me that I have found useful are the following.
• Astronomer to Data Scientist , a brief but very useful post on transitioning from academia to data science which was very helpful to me when I was getting started. [introductory level] [if you are curious what happened to her after this post, read  this interview  with her after a year and half after]
• Data School  [introductory level]
• Data Science 101  [introductory and technical level]
• Data Science Central  [introductory and advanced level]
• FlowingData  [technical and detailed level]
• R-bloggers  [technical level]

Online Courses [MOOC]:

There are many online courses that you may want to participate. Many of them are available for free and there are many which are not. Below I have listed the most popular educational websites with links to particular courses on data science. Those which I’ve found the most useful courses among all courses that I’ve taken so far are marked with [***]. They have the highest priority to be taken for an outsider of the field, in my point of view.

• [***] Machine Learning by ANdrew Ng [10-weeks, high work-load]
• The Data Scientist’s Toolbox by Jeff Leek [4-weeks, medium work-load]
• R Programming by Roger D. Peng [4-weeks, medium work-load]
• Getting and Cleaning Data by Jeff leek [4-weeks, medium work-load]
• Data Science and Machine Learning Essentials (AzurML) by Steve Elston [5 weeks, medium work-load]
• [***] Statistical Learning by Trevor Hastie [10-weeks, high work-load]
• [***] Mining Massive Datasets by Jure Leskovec [7-weeks, high work-load]
• Intro to Hadoop and MapReduce by Cloudera [4-weeks, medium work-load]
• Data Visualization and D3.js by Zipfian Academy [6-weeks, medium work-load]
• [***] Kaggle R Tutorial on Machine Learning [1-hour, low work-load]

update 2022: I recently developed a 5-weeks interactive course that quickly covers an end-to-end real-world Data Science project from data understanding and data collection all the way to modeling, evaluation and deployment. You can learn more about this new course on my website or directly through this link .

• Insight Data Science Fellows Program : A 6-weeks bootcamp on data science started in New York City. Completed or approaching completion of a Ph.D. is required. [FREE]
• The Data Incubator : A 6-8 weeks bootcamp on data science in New York and Washington D.C. [FREE]
• Microsoft Research Data Science Summer School : An 8 weeks bootcamp on data science in New York [FREE]

Be aware that since they are free and high quality and the employment rate of the graduates by popular companies are very high in these bootcamps, getting admission to them are quite competitive.

In addition to the above free bootcamps, there are several other popular data science bootcamps which are not free [~$12-16k in 2015]. After all, consider data science bootcamps as an optional choice. You don’t need to participate in these bootcamps to get a nice data science job, but if you have time and money to participate in one, do it.

Academic Graduate Programs:

Here in this note I’m targeting those who want to do a transition to data science with advanced degrees in academia and this option is not necessary in such a situation. But for the sake of completeness of the argument, I write a few lines about it.

Recently, there have been started many Master programs in various schools and universities in the US including in Columbia and Stanford University. A list of master programs in Data Science in the US is available online. A full-time master program of data science usually takes 1-2 years. Due to this relatively long duration of these programs and high costs, they are only recommended for undergraduates interested in data science or for those who already have a job in analytics teams of corporation and want to learn about data science in an academic system.

3 Steps Between you and Becoming a Data Scientist

Assuming you are an academic with solid domain expertise in your field willing to became a data scientist, there are three major steps that you should take for a smooth take-off from your field and land in to the field of data science. It definitely depends on your skills’ level when you start and how much effort you intend to put in the process, but for me, for example, it took around 18 months from the point I started my first online course on data science to the point that I got my first job offer. The following are the three major steps that I categorize all the details of this journey under those [see the below schematic].

A) Skill Development: Establish a solid data science skill set

Since this is the first and probably the most important step towards your goal, it’s very important to do it right. Because data science is an interdisciplinary field, there might be many topics that will be totally new to you and you have not seen them before. Be patient and follow an organized plan to learn the new topics as deeply as you can. Don’t worry much about the time you spend in this step. Like in any other learning curve, you are going to go back and forth several times until you learn a topic deep enough to be able to apply it elsewhere. Absorb as much as you can from the learning resources and make yourself comfortable with the new concepts such that you can easily use them in the real-world problems later on.

Choose your resources carefully and read, listen, watch, and learn as much as you can to develop and improve your basic knowledge and skills in programming, machine learning algorithms, and software toolboxes. The textbooks and online courses listed in the previous section are rich resources to get you started. I personally recommend starting with Machine Learning course in Coursera by Andrew Ng which required almost no prior knowledge of machine learning and data science and afterwards taking the more advanced course of Statistical Learning by Trevor Hastie and Rob Tibshirani of Stanford. Both of these courses are linked above in the resource section. They both cover a broad range of topics in different depths with slightly different approaches and tools which makes it an excellent strategy to learn, I think.

Look around, find, follow, and communicate with the people who are experienced in data science including the seniors in the field and also those who are willing to take the same path as you for company along the way. It is going to be a long journey and having the right people in the right moments around you will certainly give you lots of benefits during this journey. Since it’s a transition path, there will be moments of loss and darkness. Never give up and always be positive. The more informed your initial decision to take this journey is, the less lost and darkness moments you will have. Gather as much information as you can about the new field that you are willing to enter to.

In addition to the new concepts and various machine learning algorithms which are crucial for doing data science, it’s also very important to be familiar with the tools. Know a programming language very well to the master level and try to learn as much as you can from other languages to be able to use them in special circumstances. There is a long debate among data scientists on which language is the best for doing data science. After all, it doesn’t matter which programming language you use to implement your algorithms but it is important to be able to communicate with the rest of the community.

For this reason, and because of their popularity, Python and R are the most recommended languages to learn and use among all. Python has been written by computer scientists and therefore is more structured than R. But R is developed by statisticians and therefore it’s easier to learn and apply statistical concepts with it. I learned and worked with both of them, just to be on the safe side in any case. I don’t regret it.

B) Project: Start and finish a data science project

Although developing a strong skill set is the most basic step towards becoming a data scientist, and therefore the longest one too, I cannot put enough emphasis on the positive effect of performing a small data science project on your success in the next and final step which is job searching. Thinking from the point of view of a hiring manager who wants to hire you with an academic background in your first data science position, regardless of your great resume and all the long list of refereed papers on high impact journals, the first thing they would like to know is whether you are actually capable of doing a data science project from the beginning to the end. Performing a data science project prior to the job interview and showing the procedure and results is certainly a key factor which clarifies the ability and quality of your work to the interviewer and helps him/her to make his/her decision more informed and therefore more confidently.

There are many interesting data science projects out there that you can work on in this stage. Participating in an internship program of a nice company would be certainly a good idea if you have the time to do it. Keep in mind that in this case, you have to go through all the application process and paperwork to do an internship. But if for any reason doing a data scientist internship is not working for you, I recommend working on a project on your own. It is more flexible and less time consuming. If you choose so, I strongly recommend participating in one of the Kaggle competitions.

Kaggle is a platform for data scientists who are willing to participate in very interesting, real-world problems. Competitions are performed in different categories but the structure is quite the same for all the categories. It’s very simple. There is a data science problem that you are supposed to solve before a solid deadline. All training data is available to download. The solution that you are supposed to submit to the website is a single file in a standard format. It doesn’t matter what tool, method, or algorithm you have used; Kaggle will score your submitted file based on comparing it with the real solution that they have kept confidentially.

There are a few things that make Kaggle an excellent platform for you to use at this stage. First, all the problems in this website are standard real-world problems that you have several options to choose from. Second, since it is happening in a competition, there will be many others like you who are working on the same problem as you. So, you can always communicate with your fellow Kagglers to share ideas and improve your algorithms and results. Third, at the end of the competition your solution will get ranked and in the case you have done it well, you can present it as a valid achievement to show the quality of your work.

C) Job Searching: Shape up your resume and do the job search wisely

After above steps, now it’s the time to reach out to the world and show what you have got and what you can do. You’ve got all you are and ready to start searching for your dream job. However, just like previous steps this step, i.e. searching for a proper job, wouldn’t be easy either. Although data scientist jobs are highly on demand but notice, on the other hand there are many talented persons like you with higher educations out there who are also active in the job market and therefore the environment is very competitive to become a data scientist. So, to be successful in this final stage, some specific rules and strategies should be followed.

If you are coming from academia and used to have a 10-pages CV full of rocket science publications, achievements, and honors with the list of all talks and seminars that you have given to folks, burn it down and create a one (max 2) page resume instead. Make sure at the beginning of your resume you have clarified yourself, what you are and what you want. Highlight your main skills relevant to the job you are looking for which in this case is data science. List your experiences to support and justify your highlighted skills.

Keep in mind that “Data scientist” is often used as an umbrella title to describe jobs that are drastically different. Be aware and be careful about what you will be actually doing under the title of data scientist and in what industry you will be working. Data engineer, data analyst, and data scientist roles may make you confused at the first glance. This infographic describes different roles in the business of data science pretty well.

Transitioning to a new field, your prior information is your power. Do a broad and careful research on different aspects of the field and what you are interested in the most. No question is stupid or naive. Find the answers of your questions about data science jobs. 2015’s Burtch-Works report on analyzing the status and compensation of 371 data scientists in 2015 in the US is very useful in this stage i.e. job searching. They may continue publishing new reports next years as well. Analyzing 11,400 data scientists currently employed by companies known to LinkedIn, RJMETRICS has also published a review on The Status of Data Science. Read these reports carefully and enrich and update your knowledge about the field of data science. Glassdoor has also many useful features in this regard. The bottom line here is that clear up your eyes and sharp your mind on what you are going through.

For the sake of completeness, here I should write about how to search for your dream job efficiently and all different techniques that you need during this step, but since it’s a broad subject and may go beyond the scope of this note, I decided not to go far deep into this topic and refer the readers to other technical references on this subject instead.

However, I would like to briefly mention a few quick, crucial points. First, be prepared for the job interviews but don’t expect your first few job interviews to go as you expect. It always gets better in the next interviews. Take notes and use your previous experiences in the next occasions. Wrap your mind around what you have, what you are, and what you want to present in advance. It takes time and won’t be easy in the first interviews. I remember when I got a phone interview from Google, I was so frustrated and stressed out just because I was not ready for it. It got much better in the next interviews later on.

Second, work hard on your communication skills and build up your own network. If you have data scientist friends, reach out to them and ask for suggestions and get them involved in your job search process. They may also have information about job openings in their companies or in other places in their network that you want to hear about. Expand your options by connecting to others on the other side of the wall.

Third, always be connected and be updated. Internet social networks are powerful tools. Use LinkedIn , Monster , Indeed , Kaggle job board , and Glassdoor for your best. They are actually functional. Face-to-face conversations are great but do not underestimate online tools in your job search and make the best use out of them. Follow popular data scientists on social media specially those who are in your target geographical region and discover what they are up to.

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PhD Jobs: Data Scientist

Data scientist roles can be remote or in-person with a majority having flexibility in the agreements they have with employers.

I. Introduction

Core Responsibilities: Data scientists extract knowledge and insights from data using a variety of techniques and tools. They leverage programming, statistics, and machine learning to solve complex problems across various industries.

Industries: Data scientists extract knowledge and insights from data using a variety of techniques and tools. They leverage programming, statistics, and machine learning to solve complex problems across various industries.

II. Day-to-Day Tasks

Data Acquisition and Cleaning: Data scientists gather data from various sources, clean and organize it for analysis.

Exploratory Data Analysis (EDA): They perform statistical analysis, create visualizations, and identify patterns and trends in the data.

Model Building and Training: Data scientists develop and train machine learning models to make predictions or classifications based on the data.

Evaluation and Communication: They evaluate the performance of models, interpret results, and communicate insights to stakeholders.

Collaboration: Data scientists often work with engineers, data analysts, and business teams to ensure projects align with overall goals.

Example: A data scientist at a retail company might analyze customer purchase data to build a model that predicts future buying behavior. This can be used to personalize marketing campaigns and optimize product recommendations.

III. Required Skills and Qualifications

Technical skills:.

Programming expertise in languages like Python, R, and SQL.

Strong foundation in statistics and probability.

Machine learning algorithms and techniques (e.g., linear regression, decision trees, deep learning).

Familiarity with data analysis tools (e.g., pandas, scikit-learn, TensorFlow).

Experience with cloud platforms (e.g., AWS, Azure, GCP) may be beneficial.

Non-Technical Skills:

Problem-solving and critical thinking abilities.

Excellent communication skills (written and verbal) to present findings effectively.

Curiosity and a passion for learning new technologies.

Collaboration and teamwork skills.

IV. Educational Background

A PhD in a STEM field (Science, Technology, Engineering, and Mathematics) like computer science, statistics, physics, engineering, or related disciplines is a strong foundation for a data science career. However, a PhD is not always mandatory.

V. Career Path

A common career path starts with an entry-level role like Data Analyst or Junior Data Scientist. With experience, one can progress to Data Scientist, Senior Data Scientist, Lead Data Scientist, and eventually Director of Data Science. Specialization in specific areas like machine learning, natural language processing, or computer vision is also possible.

VI. Salary and Work Environment

Salary: Entry-level positions can start around $80,000 to $100,000 annually [1]. Salaries increase with experience, location, and industry, with senior data scientists earning well over $150,000 [1]. Glassdoor estimates a total compensation including base salary and bonus of $212,852 with an average base salary of $161,220 for PhD data scientists [2].

Work Environment: Data scientists typically work in office settings but may have the option to work remotely. The work can be fast-paced and involve tight deadlines, but it offers intellectual challenges and the opportunity to make a real impact with data-driven solutions.

Creating graphs and looking at data in different ways to find nuances in the data to enhance profits or lower spending are common goals of work from data scientists.

VII. Job Outlook

The job outlook for data scientists is projected to be excellent, with the U.S. Bureau of Labor Statistics expecting a growth rate of 27.9% by 2026, significantly exceeding the national average [3]. This is due to the increasing demand for data-driven decision making across various industries.

VIII. How to Transition into This Career

A PhD provides a strong foundation in research, analytical thinking, and problem-solving, all valuable assets for data scientists. However, some additional skills and experiences can strengthen your candidacy. Here are some helpful resources:

Bootcamps: Data science bootcamps offer intensive programs designed to equip individuals with the necessary skills to launch a data science career. Here are a few reputable options:

Springboard ( https://www.springboard.com/courses/data-science-career-track/ )

Flatiron School ( https://flatironschool.com/courses/data-science-bootcamp/ )

The Data Incubator ( https://app.thedataincubator.com/fellowship/apply.html )

Online Courses: Numerous online courses and tutorials cover various data science topics. Platforms like Coursera, edX, and Udacity offer a variety of options.

Personal Projects: Building a portfolio of personal data science projects showcases your skills and demonstrates your passion for the field. You can find interesting datasets online (e.g., Kaggle) to practice your data analysis and machine learning techniques.

Contribute to Open-Source Projects: Participating in open-source data science projects allows you to learn from experienced developers, gain exposure to real-world code, and build your reputation in the data science community.

Network with Data Scientists: Connect with data scientists on LinkedIn or attend industry meetups to learn about the profession and gain valuable insights.

IX. Conclusion

Data science positions often come with attractive benefits packages, including health insurance, paid time off, tuition reimbursement, and stock options.

Helpful Resources:

Kaggle: https://www.kaggle.com/

Coursera: https://www.coursera.org/

fast.ai: https://www.fast.ai/

References:

U.S. News & World Report Money: Data Scientist Salary

Glassdoor: Data Scientist Salary ( https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm )

Bureau of Labor Statistics: Occupational Outlook Handbook: Computer and Information Research Scientists ( https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm )

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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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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

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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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Opinion: The Rise of the Data Physicist

In the search for new physics, a new kind of scientist is bridging the gap between theory and experiment..

Traditionally, many physicists have divided themselves into two tussling camps: the theorists and the experimentalists. Albert Einstein theorized general relativity, and Arthur Eddington observed it in action as “bending” starlight; Murray Gell-Mann and George Zweig thought up the idea of quarks, and Henry Kendall, Richard Taylor, Jerome Freidman, and their teams detected them.

In particle physics especially, the divide is stark. Consider the Higgs boson, proposed in 1964 and discovered in 2012. Since then, physicists have sought to scrutinize its properties, but theorists and experimentalists don’t share Higgs data directly, and they’ve spent years arguing over what to share and how to format it. (There’s now some consensus, although the going was rough.)

But there’s a missing player in this dichotomy. Who, exactly, is facilitating the flow of data between theory and experiment?

Traditionally, the experimentalists filled this role, running the machines and looking at the data — but in high-energy physics and many other subfields, there’s too much data for this to be feasible. Researchers can’t just eyeball a few events in the accelerator and come to conclusions; at the Large Hadron Collider, for instance, about a billion particle collisions happen per second , which sensors detect, process, and store in vast computing systems. And it’s not just quantity. All this data is outrageously complex, made more so by simulation.

In other words, these experiments produce more data than anyone could possibly analyze with traditional tools. And those tools are imperfect anyway, requiring researchers to boil down many complex events into just a handful of attributes — say, the number of photons at a given energy. A lot of science gets left out.

In response to this conundrum, a growing movement in high-energy physics and other subfields, like nuclear physics and astrophysics, seeks to analyze data in its full complexity — to let the data speak for itself. Experts in this area are using cutting-edge data science tools to decide which data to keep and which to discard, and to sniff out subtle patterns.

Machine learning, in particular, has allowed scientists to do what they couldn’t before. For example, in the hunt for new particles, like those that might comprise dark matter, physicists don’t look for single, impossible events. Instead, they look for events that happen more often than they should. This is a much harder task, requiring data-parsing at herculean scales, and machine learning has given physicists an edge.

Nowadays, the experimentalists who manage the control rooms of particle accelerators are seldom the ones developing the tools of machine learning. The former are certainly experts; they run colliders, after all. But in projects of such monumental scale, nobody can do it all, and specialization reigns. After the machines run, the data people step in.

The data people aren’t traditional theorists, and they’re not traditional experimentalists (though many identify as one or the other). But they’re here already, straddling different camps and fields, proving themselves invaluable to physics.

For now, this scrappy group has no clear name. They are data scientists or specialized physicists or statisticians, and they are chronically interdisciplinary. It’s high time we recognize this group as distinct, with its own approaches, training regimens, and skills. (It’s worth noting, too, data physics’ discreteness from computational physics. In computational physics, scientists use computing to cope with resource limitations; in data physics, scientists deal with data randomness, making statistics — what you might call “phystatistics” — a more vital piece of the equation.)

Naming delivers clout and legitimacy, and it shapes how future physicists are educated and funded. Many fields have fought to earn this recognition, like biological physics, sidelined for decades as an awkward meeting of two unlike sciences — and now a full-fledged and vibrant subfield.

It’s the data wranglers’ turn. I propose that we give these specialists a clear identity — the “data physicists.” Unlike a traditional experimentalist, a data physicist probably won’t have much hands-on experience with instrumentation. They probably won't spend time soldering together detector parts, a typical experience for experimentalists-in-training. And unlike a theorist, they may not have much experience with first-principles physics calculations, outside of coursework.

But the data physicist does have the core skills to understand and interrogate data — complete with a strong foundation in data science, statistics, and machine learning — as well as the computational and theoretical background to relate this data to underlying physical properties.

The data physicists have their work cut out for them, given the enormous amount of data being churned out by experiments in and beyond high-energy physics. Their efforts will, in turn, improve the development of new experimentation methods, which are today often developed from simpler, synthetic datasets that don’t map perfectly to the real world.

But this data will go underutilized without a skilled cohort of scientists who can deftly handle it with new tools, like machine learning. In this sense, I’m not merely arguing for name recognition. We need to identify and then train the next generation, to tackle the data we have right now.

How? First, we need the right degrees: Universities should develop programs explicitly for data physicists in graduate school. I expect the data physicist to have a strong physics background and extensive training in statistics, data science, and machine learning. Take my own path as a starting point: I studied computational aspects of particle theory as a master’s student and took many courses in statistics as a PhD student, which led to naturally interdisciplinary research between physics and statistics/machine learning — and between theorists and experimentalists.

The right education is a start, but the field also needs tenure-track positions and funding. There are promising signs, including new federal funding to help institutions launch “Artificial Intelligence Institutes” dedicated to advancing this research. But while investments like this fuel interdisciplinary research, they don’t support new faculty — not directly, at least. And if you’re not at one of the big institutions that receive these funds, you’re out of luck.

This is where small-scale funding must step in, including money for individual research groups, rather than for particular experiments. This is easier said than done, because a typical group grant, which a PI uses to fund themselves and a student or postdoc, forces applicants to adhere to the traditional divide: theory or experiment, or hogwash. The same goes for the Department of Energy’s prestigious Early Career Award — there is no box to check for “interdisciplinary data physics.”

As tall an order as this funding is, it could be easier to achieve than a change in attitude. Physicists might well be famous for many of humanity’s greatest discoveries, but they’re also notorious for their exclusionary, if not outright purist, suspicion of interdisciplinary science. Physics that borrows tools and draws inspiration from other fields — from cells in biological physics, say, or from machine learning in data science — is often dressed down as “not real physics.” This is wrong, of course, but it’s also a bad strategy: A great way to lose brilliant physicists is to scoff at them.

Not all are skeptical; far more, in fact, are excited. Within APS, the Topical Group on Data Science (GDS) is growing rapidly and might soon become a Division on Data Science, a reflection of the field’s growing role in physics. My own excitement about working directly with data inspired me to become an “experimentalist” myself, although I realize now how restrictive that label was.

As available data grows, so does our need for data physicists. Let’s start by calling them what they are. But then let’s do the hard work: educating, training, and funding this brilliant new generation.

The author wishes to thank the Editor, Taryn MacKinney, for her work on this article, and David Shih for coining the term 'data physicist' at a recent Particle Physics Community Planning Exercise.

Benjamin Nachman

Benjamin Nachman is a staff scientist at Berkeley Lab, where he leads the Machine Learning for Fundamental Physics Group, and a research affiliate at the University of California Berkeley Institute for Data Science.

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B.S./B.A. in Physics + M.S. Data Science

At Seton Hall, students who complete an undergraduate B.S. or B.A. in Physics can also earn their M.S. in Data Science within only one additional year. 

The 3+2 Dual Degree program complements the extensive analytical and mathematical background students obtain in their undergraduate studies by further enhancing their computational knowledge. The program is designed to prepare students to be skilled and thoughtful data scientists, skilled in data management and processing; analyzing business and scientific processes; and communicating findings for effective decision making.

• B.S. in Physics
• B.A. in Physics
• Department of Physics
• M.S. in Data Science 
• Department of Mathematics and Computer Science

Both degrees can be earned in five years through the accelerated curriculum. Students first complete either a B.S. or B.A. in Physics, then take 9-12 credits of graduate courses from the M.S. in Data Science the summer before and during their senior year that count toward their undergraduate requirements.

Program Admission and Continuation

• Submit an application for the M.S. in Data Science program during the Spring semester of junior year
• Meet the M.S. in Data Science admission requirements, except having completed the undergraduate degree program, with undergraduate GPA of at least 2.75
• Have senior status (earned at least 90 credits) before taking graduate courses
• Have completed MATH 2111 Statistics for Science majors and CSAS 1113 Computing for Science majors by Spring of junior year. B.A. majors may also complete ISCI 1117 Computing for Informatics instead of CSAS 1113.
• Have at least a 3.0 GPA in the undergraduate Physics curriculum before taking graduate courses from the Data Science curriculum
• Before taking graduate courses during the fifth year, fulfill all the requirements for admission to the M.S. in Data Science, including having earned the undergraduate degree

View our detailed curriculum »

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College of Arts and Sciences

Mathematics, m.s. in statistics and data science.

The discipline of statistical sciences is concerned with the art of developing techniques to gain information and make decisions from data in the presence of uncertainty. The techniques are based on the theory and tools of the various branches of mathematics, especially probability. 

A graduate education in the statistical sciences allows one an entry into many areas of physical and social sciences, medicine, business and government. There are more than 130 universities in the US that offer graduate programs in the statistical sciences. In spite of the declining graduate enrollment in most disciplines across the country, the enrollment in statistics has remained steady over the last fifteen years. An average of 1000 M.S. degrees in Statistics and Biostatistics are awarded every year. A recent study by the NSF and DOE (NSF publication 80-78) concludes that during the 90's "the supply of scientists and engineers at all degree levels will likely be more than adequate to meet demand in all fields except computer professions, statistics and some fields of engineering".

The Program

The prerequisite for admission to the M.S. degree in Statistics and Data Science is an undergraduate degree that includes at least 9 semester-hours of calculus. Students who have not had any course in linear algebra, complex variables and advanced calculus are advised to take Lehigh's Math 205 (or 244), Math 208 (or 316) and Math 301 at the earliest opportunity. 

The M.S. in Statistics and Data Science requires 30 credit hours of graduate courses with at least 18 hours of 400-level courses. The choice of the courses must be approved by the graduate advisor. Up to 6 hours of coursework may be replaced with a masters thesis.  All students in the program must also pass a comprehensive examination on basic probability, statistics and linear algebra.

The M.S. program in Statistics and Data Science has two tracks, Statistics and Stochastic Modeling. The following is a guide for courses and electives in the two tracks.  

Please see Lehigh's catalog for  course descriptions

Statistics Track

First year recommended course sequence

• MATH 309 Probability with Applications and Simulation
• MATH 312 Statistical Computing and Applications
• STAT 342 Applied Linear Algebra
• STAT 410 (Random Processes and Applications)
• STAT 434 (Mathematical Statistics)
• STAT 438 (Linear Models in Statistics with Applications)

Second Year

Select at least 4 electives from the following (not all courses are offered every year):

• STAT 412 Advanced Applied Statistics
• STAT 462 Modern Nonparametric Methods in Statistics
• STAT 461 Topics in Mathematical Statistics
• STAT 439 Time Series and Forecasting
• MATH 450 Special topics - Programming in SAS
• STAT 465 Statistical Machine Learning
• STAT 471 Topics in Statistical Learning and Computing
• STAT 474 Statistical Practice

Stochastic Modeling Track

Recommended Courses

• Math 309 Theory of Probability
• STAT 410  Random Processes
• STAT 463 Advanced Probability
• Math 401 Real Analysis I
• STAT 434 Mathematical Statistics
• STAT 438 Linear Models in Statistics with Applications
• STAT 464 Advanced Stochastic Processes
• Math 341 Mathematical Models and Their Formulation

Select one other possible elective from the following:

• STAT 408 Seminar in Statistics and Probability (Spring)
• STAT 409 Seminar in Statistics and Probability (Fall)
• Math 320 Ordinary Differential Equations
• Math 340 Design and Analysis of Algorithms
• Math 402. Real Analysis II
• Math 407 Theory and Techniques of Optimization
• Math 430 Numerical Analysis
• Math 467 Financial Calculus I
• Math 468 Financial Calculus II
• Eco. 453 Index Numbers and Time Series Analysis
• CSE 411 Advanced Programming Techniques
• I.E. 316 Advanced Operations Research Techniques
• I.E. 339 Queuing Theory
• I.E. 409 Time Series Analysis
• I.E. 416 Dynamic Programming
• I.E. 439 Applications of Stochastic Processes

Additional Information

Current Math courses to be cross-listed in the catalog as STAT courses are as follows. We have elevated some 300 level Math courses to the level of 400 STAT courses in order to facilitate meeting the 18 credit hour requirement for M.S. in Statistics and Data Science.

• STAT 410 = Current Math 310
• STAT 434 = Current Math 334
• STAT 438 = Current Math 338
• STAT 462 = Current Math 462
• STAT 461 = Current Math 461
• STAT 463 = Current Math 463
• STAT 464 = Current Math 464

An undergraduate student will receive 4 hours of credit if enrolled in Math 310, 334, 312 or 338 but 3 hours of credit if enrolled in STAT 410, 434, 412 or 438.

For more information, please send e-mail to  [email protected] .

Program Requirements

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The 7 Best Data Science Courses That are Worth Taking in 2024

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A career in data science involves using statistical, computational and analytical methods to extract insights from data. Data scientists regularly use programming languages like Python and R alongside machine-learning algorithms and data-visualisation software.

The need for data scientists has surged across various sectors, including finance, healthcare and technology, making it a highly sought after and lucrative profession. According to the U.S. Bureau of Labor Statistics , the average annual salary for data scientists in 2023 was $108,020, while demand for them is expected to increase by 35% in the next eight years — much faster than average for all occupations.

SEE: What is Data Science? Benefits, Techniques and Use Cases

Online courses and certifications provide accessible pathways into the field, as many can fit around existing responsibilities like a day job. Such programs provide the expertise required for an individual to land their first data science role or just discover whether the career is for them. TechRepublic takes a look at the top six data science courses available in 2024 for learners with different goals and levels of experience.

• Best for a data science overview: IBM Data Science Professional Certificate - Coursera
• Best for beginner Python skills: Associate Data Scientist in Python - DataCamp
• Best for beginner R skills: R Programming A-Z - R For Data Science With Real Exercises! - Udemy
• Best for beginner applications: Applied Data Science Specialization - Coursera
• Best for mathematics for data science: Mathematics for Machine Learning and Data Science Specialization - Coursera
• Best for intermediate applications: Applied Data Science with Python Specialization - Coursera
• Best for college graduates: MITX - Statistics and Data Science with Python - edX

SEE: How to Become a Data Scientist: A Cheat Sheet

Best data science courses: Comparison table

IBM Data Science Professional Certificate - Coursera: Best for a data science overview

The Data Science Professional Certificate from IBM, hosted on Coursera, offers a great starting point for those interested in learning about data science but don’t fully understand what a career in it would entail. This course provides an overview of the tools, languages and libraries used daily by professional data scientists and puts them into practice through a number of exercises and projects. The final Capstone project also requires the student to create a GitHub account, encouraging them to familiarise themselves with the site and collaborate.

$49/£38 per month after a seven-day free trial.

Six months at ten hours a week.

• Industry recognition, as backed by IBM.
• Self-paced.
• Lacks depth, as aims to provide just foundational knowledge of theoretical data science and practical applications.

Pre-requisites

Associate data scientist in python - datacamp: best for beginner python skills.

DataCamp is another well-regarded provider of data-related courses, and one of its highest rated is titled ‘Associate Data Scientist in Python’. It sets itself apart with its unique hands-on coding exercises, one of which involves manipulating and visualising data on Netflix movies. Language-wise, this course exclusively uses Python, but introduces learners to multiple libraries including pandas, Seaborn, Matplotlib and scikit-learn. Knowledge of Python is not required for this course, as the necessary skills are taught along the way.

$13/£11 a month for full access.

Nine weeks at ten hours a week.

• More emphasis on programming skills and data manipulation techniques.
• Taught in Python, the most popular programming language .
• Less depth in theoretical elements of data science.
• Python-specific knowledge may not translate to different environments.

R Programming A-Z - R For Data Science With Real Exercises! - Udemy: Best for beginner R skills

While many data science courses are taught with Python due to its popularity and simplicity, ‘R Programming A-Z’ on Udemy is aimed at learners looking to get to grips with R and RStudio. R is a powerful language used frequently in data science for handling complex data sets. This course assumes no prior knowledge and starts with the very basics of R programming, including variables and for() loops, before looking at matrices, vectors and more advanced data manipulation. Large projects that help cement learning use real-world financial and sports data.

$109.99/£69.99.

10.5 hours of lectures + exercises.

• Specific to R and RStudio.
• Removes the steep learning curve often associated with R.
• Relatively small focus on data science and machine learning.
• Taught on a Mac and instructions for Windows devices are not always clear.

Applied Data Science Specialization - Coursera: Best for beginner applications

“Applied Data Science Specialization,” another course by IBM, fast tracks data science beginners towards skills with real-life applications. Python skills for data analysis and visualisation are taught assuming no prior knowledge of the language and are then put into practice in the interactive labs and projects. These cover the extraction and graphing of financial data, creation of regression models to predict housing prices and visualisation of data treemaps and line plots on Python dashboards. By the end of the course, participants should have solidified their practical Python skills to the extent that they can confidently explore more advanced topics like big data, AI and deep learning.

$49/£38 per month after a seven day free trial.

Two months at ten hours a week.

• Appropriate for beginners.
• Fast tracks learners to practical applications in data science.
• Lack of foundational knowledge provided.

Mathematics for Machine Learning and Data Science Specialization - Coursera: Best for mathematics for data science

As the title suggests, this course from DeepLearning.ai has a particular focus on mathematics for data scientists. Mathematics underpins the profession and is essential for understanding algorithms, cleaning data, drawing insights, visualisation, evaluating models and more. The course covers the fundamental mathematical toolkit of machine learning, including calculus, linear algebra, statistics and probability. Learners say it provides a good entry point into the theory of data science and the lab exercises are practical.

Six weeks at ten hours a week.

• Mathematics covered relevant to applied data science.
• Does not get into lots of depth on each topic.

A high school level of mathematics and a basic knowledge of Python is recommended.

Applied Data Science with Python Specialization - Coursera: Best for intermediate applications

Similar to the IBM “Applied Data Science Specialization” on Coursera, this course does not teach the fundamentals of programming. Instead, it launches straight into applying techniques related to machine learning, data visualisation and text analysis. What differentiates the course is that it is designed for those that already have a basic understanding of Python but want a more in-depth introduction to real-world applications within data science. Key libraries such as Pandas, Matplotlib and Seaborn are used for applied charting, machine learning and text mining. It is led by professors from the University of Michigan via five modules of video lectures, notes and exercises.

Four months at ten hours a week.

• Concentrates on data science applications of Python.
• Requires knowledge of Python.

Background in basic Python or programming required.

MITX - Statistics and Data Science with Python - edX: Best for college graduates

The “Statistics and Data Science with Python” course presented by the Massachusetts Institute of Technology is by far the most comprehensive course featured on this list. The so-called “MicroMasters” takes learners over a year to complete and prepares them for their first career in data science. It provides a graduate-level introduction to concepts such as statistical inference and linear models, as well as practical experience building machine learning algorithms. It is designed to fit around a day job or university study while not compromising on the level of content.

$1,350/£1,186.

One year and two months at ten hours a week.

• Comprehensive.
• Prepares learners for data science jobs.
• Large time commitment required.
• Requires high-level mathematical knowledge,

University-level calculus and comfort with mathematical reasoning and Python programming are recommended.

What is the difference between data analysis and data science?

The key difference between data analysis and data science is that the former primarily looks to interpret existing data, while the latter involves creating new ways of doing so.

Data analysis focuses on examining datasets to identify trends, draw conclusions and support business decisions. It involves cleaning, transforming and modelling data to extract useful information, often using tools like Excel and SQL. It is performed by data analysts who are typically hired into a wide range of industries, including marketing firms, government agencies, healthcare providers, financial institutions and more.

Data science, on the other hand, integrates data analysis with advanced machine learning algorithms, predictive modelling and big data technologies. Data scientists often develop new tools and methods to handle complex problems and derive insights from large-scale datasets. Skills required for this include proficiency in programming languages such as Python and R, as well as a deeper understanding of statistical methods and machine learning.

SEE: 10 Signs You May Not Be Cut Out for a Data Scientist Job

Is data science still in demand in 2024?

Data science remains in high demand in 2024. The IDC estimates that the amount of data worldwide will reach 291 zettabytes by 2027, and as growth continues, more data professionals will be needed to manipulate and interpret it. Furthermore, many of the key industries within which data scientists work are expanding, such as AI, machine learning and the Internet of Things, while others provide core services such as healthcare, energy, finance and logistics. Salaries also reflect this high demand as, according to Glassdoor , the average base pay of a data scientist in the U.S. is $113,000.

Are data science courses worth it?

Opinions on online data science courses vary within the industry. For some, there are enough free resources available through platforms like YouTube to render paid courses unnecessary. They may also argue that there is no substitute for hands-on experience, and that even beginners should learn the necessary skills by downloading an open-source dataset and attempting to manipulate it themselves.

However, the key to learning anything new is persistence, and it can be difficult to remain motivated without a defined learning programme to follow, coursemates to connect with or a course fee at risk of going to waste. For individuals with a tendency to start projects but not finish them, an initial investment in a structured course may provide the motivation they need. Many paid courses also give direct access to qualified instructors who can provide tailored help that would otherwise not be available.

Ultimately, there are certainly opportunities to break into data science without taking any type of online course. However, if structured learning provides the skills or motivation you desire, then the investment may be worth it.

Methodology

When assessing online courses, TechRepublic examined the reliability and popularity of the provider, the depth and variety of topics offered, the practicality of the information, the cost and the duration. The courses and certification programs vary considerably, so be sure to choose the option that is right for your goals and learning style.

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School of Data Science to Launch Ph.D. Program, Formally Joins College of Computing and Informatics

UNC Charlotte’s School of Data Science will soon expand its academic offerings with the establishment of a Doctor of Philosophy in Data Science. Approved by the UNC System Board of Governors in May 2024, the degree program will enroll its first cohort of students in fall 2025, pending approval of the Southern Association of Colleges and Schools Commission on Colleges. 

Established in response to the skyrocketing growth of the data science industry in North Carolina and globally, the new doctorate will offer two pathways for students, providing training for both future industry practitioners and university educators. This transdisciplinary program will emphasize the core technical skills of machine learning, artificial intelligence and statistics along with the social implications and ethics of data use. The program builds on the Master of Science and Bachelor of Science programs offered by the school. Its establishment is the latest example of the University’s commitment to data science, coming over 10 years after the founding of UNC Charlotte’s Data Science Initiative.

 “UNC Charlotte is always working to add and evolve academic programs with an eye toward the future. The creation of the new data science doctoral program is the latest example of our ongoing efforts to align our curriculum to the demands of industry and will allow our thriving School of Data Science to further build on its track record of interdisciplinary innovation,” said Jennifer Troyer, provost and vice chancellor for academic affairs.

The new Ph.D. will become UNC Charlotte’s 25th doctoral degree program.

A new college home and transition in leadership The doctoral program will be enhanced by the School of Data Science formally joining the University’s  College of Computing and Informatics . 

This new structure will support and amplify the school’s ongoing mission to foster interdisciplinary research and partnership across the University, all while providing SDS with the institutional structure necessary for continued faculty expansion, student growth and research excellence as the school continues to bolster its position as an innovative data science institution working to push the field forward. The shift also will allow SDS and CCI to more effectively support the North Carolina General Assembly’s $41.2 million investment toward “Engineering North Carolina’s Future,” in service of the initiative’s call for investing in data science, cybersecurity and engineering efforts across the state.

As part of this transition, the school’s current Director of Research Dongsong Zhang was named its new interim executive director, effective May 15. Zhang is the Belk Endowed Chair in Business Analytics in the Belk College of Business. Founding Executive Director Doug Hague will work closely with Zhang throughout the upcoming academic year during the transition, as Hague begins a new role as executive director of corporate engagement for UNC Charlotte. In his new position, Hague will work hand-in-hand with University leadership, the Division of University Advancement and external partners to build relationships that strengthen UNC Charlotte’s connection with the business community and create additional opportunities for collaboration.

“With the newly approved data science doctoral program and the evolution of the School of Data Science’s relationship with the College of Computing and Informatics, SDS continues to strengthen its position as a leading data science program,” said Bojan Cukic, dean of the College of Computing and Informatics. “I am excited to continue to work alongside Dongsong Zhang in the months ahead as he and his team work to chart the school’s future. We are also extremely grateful for Doug Hague’s bold, thoughtful leadership of SDS over the years. He has played an instrumental role in the school’s incredible success, and we look forward to continued partnership with him in his new role as he works to foster innovation and industry collaboration to the benefit of our University.”

The UNC Charlotte Data Science Initiative was established in 2013. That initiative ultimately grew into the School of Data Science, founded in 2020. The Carolinas’ first School of Data Science, SDS is committed to excellence in education, research and community engagement to shape and lead the future of data science education, research and practice. Since its inception and to this day, the School of Data Science is an interdisciplinary partnership among UNC Charlotte’s College of Computing and Informatics, the Belk College of Business, the College of Humanities & Earth and Social Sciences, the College of Science, the College of Health and Human Services and the William States Lee College of Engineering.

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Position Summary

The Earth and Planets Laboratory at Carnegie Science is seeking a  postdoctoral research associate to study the fundamental physics of ferroelectric and multiferroic materials using first-principles based methods.

The successful scientist will use density functional theory and machine learning to perform molecular dynamics and other simulations to compute and optimize properties of new and known materials to computationally design better materials, and help interpretexperiments. An experimental component of the research is also possible, using optical, X-ray, magnetic, and dielectric measurements to better understand and improve materials. 

Minimum requirements for skills/experience/education:

• Experience with density functional theory (DFT), (c) a thorough understanding of condensed matter science (chemistry, physics, materials science, or mineral physics); (d) good writing and communication skills; and (e) a proven publication record.
• We are a multidisciplinary laboratory, so interest in the broad range of research at the Earth & Planets Laboratory is desirable.

Experience with first-principles molecular dynamics and machine learning is highly desirable.

How to apply

Applicants should send the following documents

• Bibliography
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The position is available immediately, and applications will be taken until the position is filled.

Physics Today has listings for the latest assistant, associate, and full professor roles, plus scientist jobs in specialized disciplines like theoretical physics, astronomy, condensed matter, materials, applied physics, astrophysics, optics and lasers, computational physics, plasma physics, and others! Find a job here as an engineer, experimental physicist, physics faculty, postdoctoral appointee, fellow, or researcher.

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2024 graduate – sw engineer/data scientist/ontologist – threat analytic systems.

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Are you excited about applying your education toward building creative solutions for our nation’s greatest challenges?

Are you searching for a collaborative environment that prioritizes impact, innovation, and personal development?

If so, the Threat Analytic Systems (QAI) Group is looking for someone like you to join our APL team!

QAI is a team of software engineers, data scientists, and knowledge representation experts who partner with experts across a wide range of domains including countering weapons of mass destruction (CWMD), special operations, intelligence analysis, biothreat security, online disinformation, and disease surveillance. Our goal is to research and build innovative AI-enabled systems that can identify and prioritize relevant data in a timely and scalable manner and accelerate our sponsors’ ability to observe and act on threat activities.

We need talented engineers, scientists, and ontologists with creativity, curiosity, and a strong drive for learning to succeed!

As a member of our team you will…

• Design, implement, test, and deploy algorithms, machine learning models, and AI-enabled systems in a dynamic, fast-paced, and team-oriented environment.
• Interact with sponsors, analysts, and other end users to understand their mission goals, gather requirements, and innovate new ways to improve efficiency.
• Survey academic literature, propose new research, engineer solutions, and utilize DevOps/MLOps strategies to overcome deployment challenges.
• Document and present work on current research and development activities

You meet our minimum qualifications if you have…

• A Bachelor’s degree in computer science, mathematics, engineering, or related technical field.
• Have strong problem solving, analytical, and organizational skills, in addition to excellent written and verbal communication skills.
• Have experience working successfully within a team environment

For software engineers:

• Experience in computer science fundamentals and Java, Python, JavaScript, and web development

For data scientists:

• Experience in Python
• Familiarity with machine learning and scientific libraries (e.g., PyTorch, Tensorflow, Scikit-Learn, Pandas, Numpy)
• Interest in working with engineers to integrate models and algorithms into analytic pipelines

For ontologists:

• A foundation in formal logic

• Are able to obtain an Interim Secret Clearance by your start date and ultimately obtain Top Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

You’ll go above and beyond our minimum requirements if you have…

• A Master’s degree in computer science, mathematics, engineering or related technical field.
• Familiarity with Agile software development practices, full life cycle software development, or feature development
• Familiarity with continuous integration and continuous deployment (CI/CD) best practices
• Experience with machine learning libraries and techniques
• Experience with mobile development and Kotlin
• Experience creating experiments, developing approaches, and testing hypotheses for data-driven challenges
• Familiarity with Python best practices
• Familiarity with foundational machine learning approaches
• Experience with information extraction and natural language processing techniques
• Experience building RDF/OWL ontologies

Why work at APL?

The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation’s most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.

At APL, we celebrate our differences and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL’s campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at www.jhuapl.edu/careers.

APL is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law.

APL is committed to promoting an innovative environment that embraces diversity, encourages creativity, and supports inclusion of new ideas. In doing so, we are committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact [email protected]. Only by ensuring that everyone’s voice is heard are we empowered to be bold, do great things, and make the world a better place.

We respectfully acknowledge the University of Arizona is on the land and territories of Indigenous peoples. Today, Arizona is home to 22 federally recognized tribes, with Tucson being home to the O'odham and the Yaqui. Committed to diversity and inclusion, the University strives to build sustainable relationships with sovereign Native Nations and Indigenous communities through education offerings, partnerships, and community service.

• Equity & Inclusion

PhD student aims to improve AI for underserved languages and communities

Hellina Hailu Nigatu always loved math and physics – really any field that let her calculate things. Then she found computer science. By the time she'd started her PhD at UC Berkeley, she saw how computing skills could address a range of issues from healthcare to women's rights.

She learned in classes about concepts in Natural Language Processing and large language models developed in English. But Nigatu, who is Ethiopian, found that most of the methods she learned wouldn’t work in other languages she knew. The data didn’t exist on the internet to build chatbots for the Tigrinya language, like the ones already changing how English speakers live and work. The safeguards blocking some harmful content online weren’t effective for Amharic-speaking users.

“This is what happens when you have diversity in computer science,” said Nigatu, who is starting the fourth year of her doctorate program. “Almost all of my projects are inspired by personal experience.”

“I could be an English speaker who improves machine translation, speech recognition and whatever other technology in Amharic by  looking at performance on some evaluation metric,” she said. “But if I did not speak this language, if I was not from Ethiopia, if I was not impacted by this, I wouldn’t have the context to understand the nuanced problems that go beyond automatic metrics.”

The artificial intelligence boom is rapidly transforming modern life. But with a lack of diversity in who informs and develops these technologies and the disparities in the existing data being used, these tools could cement and exacerbate global inequalities.

Nigatu is an up-and-coming expert combating that risk through research and mentorship. She hopes to develop tools that are informed by and useful for communities, including her family and friends, who speak languages that have little available data online.

Preserving languages and cultures that have less data online

Nigatu graduated from Addis Ababa University with a bachelor’s of science in electrical and computer engineering. She graduated from Berkeley with a master’s of science in computer science and is being advised for her PhD by Berkeley Department of Electrical Engineering and Computer Sciences’ Sarah Chasins and John Canny .

The barriers Nigatu has faced in applying Natural Language Processing tools and techniques to languages with less data available – or low-resourced languages – inspired one of her latest research papers. Early in her doctorate program, she decided to build language models in Amharic and Tigrinya. But when she went to Wikipedia, a common source of data for language processing work, she found there weren’t enough entries for either language to develop those models. The entries that did exist were often not high quality enough to use, either.

Her recent paper from the ACM Conference on Human Factors in Computing Systems – “ Low-Resourced Languages and Online Knowledge Repositories: A Need-Finding Study ” – looked for an answer to why that hurdle existed. Nigatu and co-authors analyzed Wikipedia forum entries by experienced contributors and conducted interviews with novice contributors to Wikipedia for three languages – Amharic, Tigrinya, and Afan Oromo.

They found several intersecting challenges that made it difficult for people to add entries to the platform in these languages. Wikipedia contributors struggled with the design of the platform or the lack of language-support tools like keyboards for languages that weren’t based in Latin. They also ran into challenges due to socio-political issues, such as the lack of scholarly work freely accessible on the internet that they could cite in their entries.

The dearth of data on currently recognized online knowledge repositories like Wikipedia presents major risks, Nigatu said. These platforms are being used to develop next generation tools that will be foundational to how we live our lives moving forward. The platforms are already preserving and sharing information and cultural values in higher-resourced languages like English, but aren’t offering equally useful or accessible data for other languages, she said. 

“We should be building from the ground up with community values in mind.”

“We should be building from the ground up with community values in mind, so that we preserve these languages and these identities without expecting them to adapt to the default,” said Nigatu. These platforms must “serve their needs” and not exploit them or their data, she said.

Improving the safety of the online experience

Nigatu has also found research questions through her own lived experiences. She noticed that when she searched for benign terms in Amharic on YouTube, she’d receive policy-violating, pornographic videos as results. When she dug deeper, she found this was a broader pattern for Amharic search results on the platform. She turned her discovery of this issue into a paper.

Niagatu and co-author Inioluwa Deborah Raji collected data from, and conducted interviews with, users of the platform centered on the YouTube results for Amharic searches. In their paper “ I Searched for a Religious Song in Amharic and Got Sexual Content Instead”: Investigating Online Harm in Low-Resourced Languages on YouTube ,” they found that content moderation was a big problem. Very few human content moderators focus on any non-English language, and automated content moderation doesn’t work well for low-resourced languages.

They also found people posting on YouTube used techniques to evade moderation, like using “doctor” or medical terms in their channel names in order to seem like they were offering health advice. And by analyzing the comments, they found that migrant workers located in Middle Eastern countries seeking medical advice were often tricked into clicking on pornographic content. Amharic users interviewed for the paper said they felt disempowered and devalued.

“This project showed that when we ignore a huge population in how we design and how we build technologies, the result is that these populations are disproportionately burdened with the harms,” said Nigatu of the paper published in Proceedings of ACM Conference on Fairness, Accountability, and Transparency .

“When we ignore a huge population in how we design and how we build technologies, the result is that these populations are disproportionately burdened with the harms.”

But for Nigatu, this project also highlighted something else. As she was working on this paper, she had to overcome significant pushback and questions from others about whether this was a problem and whether there was real harm being done here. It made her question whether to tackle the paper at all. She credits Raji for encouraging her to continue.

“When I say ‘online harm,’ people are like, ‘Oh, did it lead to genocide? Did people die from it?’ And I'm like, ‘I mean, harm doesn't have to get to that stage for it to be harmful,’” said Nigatu. “The standards that we have for what's acceptable to certain communities and what's not is very, very different.”

Expanding the field moving forward

Nigatu is already onto her next project. In June, she received a diversity, equity and inclusion scholarship from the ACM Conference on Fairness, Accountability, and Transparency. 

Using this recognition, she will work to improve machine translation for low-resourced, Ethiopian languages. She will also fund and mentor female students from Ethiopia to work on that research with her. This builds on past mentorship she’s offered to Ethiopian women in the computer science field.

“I know how hard it was for me to try to do research when I was there, so I do whatever I can do to try to bridge that gap,” Nigatu said.

While technology is rapidly advancing, diversifying who creates computing tools will take time. In the meantime, she hopes researchers will speak with members of underrepresented communities to hear their experiences and insights.

She feels the responsibility of being an Ethiopian female researcher every day. When she interviewed with Chasins to be her PhD advisor at Berkeley years ago, Chasins asked what would get Nigatu out of bed on the mornings she was too exhausted to do the work. Nigatu’s answer was simple.

“I don't have the luxury of saying, ‘Oh, I can’t do it today,’ because it's practically me. I am the person who's at the end going to be using these technologies. It's my friend. It's my sisters. It's everyone that I'm close to,” she said. 

“I pull a lot of the motivation for my work from my personal experience and from the experiences of those around me,” Nigatu said. “It keeps me going."

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Hellina Hailu Nigatu is a computer science PhD student at UC Berkeley. (Photo courtesy of Hellina Hailu Nigatu)

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Current PhD - Data Science Internship - Summer 2025

Key Role Details

This is a full-time paid internship program spanning ten weeks from June through August 2025.  

Participation in the internship requires that you are located in the continental United States with in-person attendance at your assigned location, in accordance with Capital One’s hybrid working model , for the duration of the program.

Interns will be evaluated for a full-time position with their team and will work with their manager on identifying a start date upon graduation

This is a paid internship. This is a limited-time internship position, and Capital One will not sponsor a new applicant for employment authorization for this position. However, a full-time Data Science role, for which you may be considered upon completion of the internship (subject to business need, market conditions, and other factors) is eligible for employer immigration sponsorship.   

Basic Qualifications

Currently pursuing a PhD with an expectation that your degree will be obtained by August 2027 or earlier 

Must be continuing in the same course of study of your PhD degree following completion of the internship

At least 6 months experience or academic work using open source programming languages for data analysis

At least 6 months of experience or academic work using machine learning techniques

At least 6 months of experience with either Python, R or SQL

Preferred Qualifications:

Experience or academic work in inferential statistics

Experience or academic work with relational databases

Experience or academic work with large scale data analysis

Data Science Internship - 2025

As a Data Scientist at Capital One, you’ll be part of a high-performing team that’s embracing the latest in computing technologies to unlock opportunities that help everyday people save money and improve their financial lives. You'll help our customers solve their financial services challenges by using groundbreaking techniques.  You'll make valuable contributions from day one by continuously learning, engaging in diverse sets of experiences, and building close-knit relationships across the company. Role / team focus areas could include supporting machine learning, deep learning, or quant initiatives across the enterprise. 

On any given day, you might:

Evaluate open-source and internally-developed modeling and analytics tools using real business data

Integrate internal data with external data sources and APIs to discover and implement actionable insights

Design and craft data science models to communicate solutions to customers and company leadership

We'd love to find someone who is…

Intellectually curious. You ask why, you explore, and you are excited to imagine and create new ideas by inventing self-adaptive models or by tapping into unstructured data sources. You love mining data for insights into behaviors, intent and sentiment

A builder. You are passionate about delivering better experiences and better products to our customers and have a deep sense of ownership for your craft.

An experimental scientist. You love putting on your lab coat and trying new things, new combinations of tools, techniques and feature engineering approaches even if you sometimes fail.

A Communicator.  You seek to connect with teammates and business partners to share technical ideas that resonate with audience needs across verbal and written channels. 

Are you ready to join a community of talented individuals who embrace customer problems and use their passion and capabilities to make a difference every single day? If you're interested in a long-term career at Capital One, the Data Science internship could be a great way to begin your career journey!

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

New York City (Hybrid On-Site): 

$181,000 - $181,000 for Data Science PhD Intern

San Francisco, California (Hybrid On-site): 

$191,000 - $191,000 for Data Science PhD Intern

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at [email protected] . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

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Calculating faster: Coupling AI with fundamental physics

by Stefanie Terp, Technical University of Berlin

Atoms are complex quantum systems consisting of a positively charged nucleus surrounded by negatively charged electrons. When multiple atoms come together to form a molecule, the electrons of the constituent atoms interact in a complicated manner, making the computer simulation of molecules one of the hardest problems in modern science.

Researchers from the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin and Google DeepMind have now developed a novel machine learning algorithm which enables highly accurate simulations of the dynamics of a single or multiple molecule on long time-scales. Their work has now been published in Nature Communications .

These so-called molecular dynamics simulations are important to understand the properties of molecules and materials and have potential applications in drug development and material design (e.g. for use in solar panels and batteries). Traditional methods to compute the interactions of electrons rely on finding solutions of the so-called Schrödinger equation.

The Schrödinger equation describes the energy levels that a quantum system—e.g. atoms or molecules—can assume. This is a notoriously difficult task, and finding a solution for molecules containing more than a few dozen atoms may take several days—even on powerful computers. To make matters worse, for running molecular dynamics simulations over long time-scales, the Schrödinger equation needs to be solved thousands or even millions of times, making the computational cost quickly exceed the compute resources that are available today.

"The simulation of such interactions and the resulting predictions for complex processes like protein folding or the binding between individual molecules is a long-held dream of many chemists and material scientists, and would save many expensive and labor-intensive experiments," explains BIFOLD researcher Thorben Frank.

In recent years, machine learning (ML) methods have brought this dream within reach. Instead of explicitly solving the Schrödinger equation, they can learn to directly predict the overall outcome of the relevant electronic interactions at the atomistic level, with greatly reduced computational cost.

The difficulty is then shifted to finding efficient algorithms for "teaching" the machine learning system how the electrons interact without modeling them explicitly. To reduce the complexity of this task, many learning algorithms use the fact that physical systems follow so-called invariances.

Simply put, certain properties of molecules stay the same when molecules are moved in space but the relative distances between individual atoms stay fixed—meaning the machine does not need to learn anything new in these cases. However, the way these invariances are typically incorporated into ML models is computationally expensive, ultimately limiting the speed with which the models can perform molecular dynamics simulations.

To address this shortcoming, the BIFOLD scientists have devised a new learning algorithm that decouples invariances from other information about a chemical system at the outset. Unlike previous methods that required extracting invariant components from each operation within the model, this new approach simplifies the process. Now, the ML model can reserve the most complex operations for the physical information that really matters and drastically reduce the overall computational cost.

"Simulations that required months or even years of computation on high-performance computer clusters, can now be performed within a few days on a single computer node. The leap in efficiency allows long-time scale simulations, which are necessary for understanding the structure, dynamics and functioning of atomistic systems. It thus enables deeper insights into the most complex and fundamental processes of nature," says BIFOLD researcher Dr. Stefan Chmiela who spearheaded the research project.

In the future, the accurate simulation of the interaction of molecules with proteins in the human body could allow researchers to develop new drugs without the need to perform experiments—saving time and money while at the same time being more environmentally friendly.

To showcase potential applications of the algorithm, the team used the new ML method to identify the most stable version of docosahexaenoic acid, a fatty acid which is a primary structural component in the human brain. This task requires scanning tens of thousands of potential candidates with high accuracy. So far, such an analysis would have been infeasible with traditional quantum mechanical methods.

As noted by Prof. Dr. Klaus-Robert Müller, BIFOLD co-director and Principal Scientist at Google DeepMind, "This work demonstrates the potential of combining advanced machine learning techniques with physical principles to overcome long standing challenges in computational chemistry. It continues a critical line of research which puts focus on scaling ML approaches towards realistic chemical systems of practical interest."

Dr. Oliver Unke, Senior Research scientist at Google DeepMind comments, "Earlier this year, we succeeded in scaling models to thousands of atoms, but with new advancements like this, moving to even larger numbers of atoms may become possible."

While simulations with tens to hundreds of thousands of atoms are now becoming accessible, some structures consist of millions of atoms or more. The next generation of algorithms will need to be able to simulate such system sizes accurately, which requires a correct description of additional, complex, long-range physical interactions.

Journal information: Nature Communications

Provided by Technical University of Berlin

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Who's running in Wyoming? These state house races are key to 2024 election.

All 62 seats in Wyoming's state house are up for grabs in this election cycle . In the supermajority Republican state , the August 20 primary will reveal whether the state will continue its slide to the right or if the hardline conservative wave of recent years has crested.

Who's running for House District 4?

State House District 4 is a long, narrow slab running through parts of Eastern Wyoming's Laramie and Platte counties .

Pastor Jeremy Haroldson has represented the district since 2021. Haroldson is a rising star in the state's Republican party, having recently served as the chairman of the state's Republican Convention , and was the sponsor of a bill this year aiming to repeal gun-free zones across the state. Haroldson drew ire in 2021 for controversial remarks on slavery .

Haroldson faces challenger Jeffery Thomas, who has served as Guernsey's fire chief for over 13 years. In an email to USA TODAY, Guernsey listed his priorities, including support for the Second Amendment, better funding for EMS Services , and support for Wyoming's core industries.

Who's running for House District 7?

State House District 7 is located in northern Cheyenne, just on top of the Cheyenne Regional Airport, and covers part of Laramie County.

Incumbent Bob Nicholas, who has served in the Wyoming legislature since 2011, will face Kathy Russell , the Wyoming GOP executive director.

Nicholas, an attorney who invites the entire 93-person legislature to his house for a seafood boil at the end of every session, is seen as symbolic of the party's more moderate wing. Nicholas has been a regular figure in budget negotiations during his legislative tenure and has won every primary and election since 2010 by at least six percentage points.

Russell has served in her executive director role since 2018, making her front and center in the Wyoming GOP's push to the right in recent years. A former biologist, Russell notched 16 years in Wyoming's coal industry and entered Wyoming politics as a precinct committeewoman for the Converse County Republican Party in 2008.

Who's running for House District 9?

State House District 9 is located in Laramie County and covers much of Northeast Cheyenne.

Incumbent Landon Brown , who has served in the Wyoming House since 2017, faces Exie Brown. Although they share the same last name, there's no family connection.

Landon, development director at the Cheyenne Regional Medical Center Foundation, is a frequent target of the state's further right politicians. Landon has coasted to victory in all past elections, his chances of winning this year are looking slim, primarily due to his recent defense of a convicted rapist in Wyoming court.

Exie, a retired Air Force officer, and current small business owner is running on issues including school choice, property tax reform, and protecting the Second Amendment.

Perry Helgeson, a third candidate for the seat, withdrew from the race, telling USA TODAY he was worried about "splitting the conservative vote" and handing victory to Landon.

Who's running for House District 24?

State House District 24, in Park County, slices out the Western half of Cody, and juts into some of Wyoming's most stunning landscapes, including portions of Yellowstone National Park.

The exit of state Representative Sandy Newsome, who served the district since 2019, has left an open seat in one of Wyoming's most politically contentious regions , and sets up a showdown between two politically well-seasoned Wyomingites.

Nina Webber, the current Wyoming National Committeewoman for the Republican Party, will square off against Matt Hall, the Mayor of Cody.

Webber, who lost to Newsome in the 2020 and 2022 Republican primaries, has placed great emphasis on not letting Wyoming turn into a "blue" state like Colorado or California. Other stated priorities include combatting rising property taxes and the "indoctrination of children," preventing Medicaid expansion in Wyoming and voting against any "hate crime" bills or bills supportive of marijuana legalization.

Hall has served as Mayor of Cody since 2017 and is the president of the Wyoming Association of Municipalities. He has earned Newsome's endorsement and says he will focus on overcoming dysfunction and obstruction in the state capitol.

Who's running for House District 30?

State House District 30, in Sheridan County, takes a bite out of Northern Wyoming and presses up against the Montana border. Mark Jennings, who has served the district since 2015, is vacating the seat to run against Barry Crago for Senate District 22. Political activist Gail Symons , founder of Wyoming politics blog Civics307, and Thomas Kelly, chair of the Department of Political and Military Science at the online, for-profit American Military University, are entering the ring to represent House District 22.

Symons, a U.S. Navy veteran, has used the data-heavy Civics307 blog to track bills passing through the Wyoming legislature and has testified on elections in front of legislative subcommittee meetings. She laments the prominence given to national culture war issues and less to problems of substance impacting local voters.

Kelly, endorsed by Jennings, wrote in an email to USA Today that he was motivated to run by seeing "campaigning as Republicans and governing as Democrats," and that he "has seen firsthand how a red state turns blue."

Who's running for House District 43?

House District 43 runs southeast of Cheyenne down to the Colorado border, taking up part of Laramie County. Incumbent, former community college professor and current healthcare professional Dan Zwonitzer has represented the district since 2005. He was first elected at 24 and faces a strong challenge to his candidacy from Ann Lucas , former vice president of a local credit union.

The longest-serving member of the House and the only openly gay member of the Wyoming legislature, Zwonitzer currently serves as the chair of the House Labor, Health & Social Services committee. Far-right voices in the state regularly characterize Zwonitzer as a "RINO" for his more moderate voting record. He is not shy to criticize a growing further-right wing of Wyoming's Republican Party as driven more by anger than by policy.

Lucas, recently endorsed by the Wyoming Freedom Caucus, moved to Wyoming in 2002, partially because of the "wholesome, conservative culture. Lucas stresses her financial background, familiarity with budgets and taxes, and support for fiscal conservatism. Lucas and her husband are members of the Laramie County Republican Party Central Committee.

Who's running for House District 50?

State House District 50 cuts out the eastern part of Cody, and extends northwest through Park County to the Montana Border.

Incumbent Rachel Rodriguez-Williams , who has served the district since 2021, faces a challenge from attorney David Hill .

A former law enforcement officer and endorsed by the Wyoming Freedom Caucus, Rodriguez-Williams is one of the Cowboy State's best-known abortion opponents , recently helmed Cody's 2024 Right to Life Walk, and was the sponsor of 2022's Life is a Human Right Act, one of Wyoming's abortion bans currently held up in court by constitutional challenges. Rodriguez-William's voting record is hard-line conservative, and she earned her endorsements from organizations, including Wyoming Right to Life and Gun Owners of America.

Hill's campaign decries infighting in the Wyoming legislature, and states that "legislators who vote in groups surrender their vote to special interests.

While Hill supports "narrowly tailored" exceptions for pregnancies involving the life of the mother, rape or incest, or the baby not surviving beyond birth, he is clear that "laws governing abortion should be carefully drafted to ensure that they will survive constitutional challenges." Hill also lists suicide prevention, school choice, and Second Amendment protections as policy priorities.

Who's running for House District 57?

State House District 57 covers part of Natrona County, taking a chunk of south Casper, Wyoming's oil and gas capital.

Incumbent Jeanette Ward , who has represented the district since 2023, is being challenged by education professional Julie Jarvis .

Ward, a doula and a self-described "political refugee" from Illinois, has made efficient use of her time in Wyoming, swiftly becoming one of the Freedom Caucuses' most prominent lawmakers. Ward sponsored the failed " What is a Woman " Act, which was condemned by civil rights organizations such as the ACLU of Wyoming. Ward was also a major supporter of bills against mask and vaccine mandates, and maintains hardline positions on abortion, the Second Amendment, and other conservative issues.

Jarvis is the Director of Teaching and Learning at Natrona County Schools, is running on the tagline of "returning Wyoming to civility," with campaign materials repeatedly stressing her lifelong Wyoming ties, and making references to the presence of "national interest groups" in Wyoming - all without directly naming Ward, or the Freedom Caucus. Jarvis lists conservative staples such as Parents' Rights', the Second Amendment, and limited government as core values.

Cy Neff reports on Wyoming politics for USA TODAY. You can reach him at [email protected] or on X, formerly known as Twitter,  @CyNeffNews

Pioneering physicist finally receives her doctorate at the age of 98

July 22 (UPI) -- A physicist who dropped out of post-graduate school to raise her family 75 years ago was awarded an honorary PhD at the age of 98.

Rosemary Fowler was studying at the University of Bristol in 1948 when she discovered the kaon particle, but she left academia before earning her doctorate when she married fellow physicist Peter Fowler in 1949. Advertisement

Sir Paul Nurse, the chancellor of the University of Bristol, said Fowler "paved the way for critical discoveries that continue to shape the work of today's physicists, and our understanding of the universe."

Fowler was 22 when she discovered a particle that decayed into three pions. This particle was later dubbed the kaon.

"I knew at once that it was new and would be very important. We were seeing things that hadn't been seen before -- that's what research in particle physics was. It was very exciting," she said.

Fowler was made an honorary Doctor of Science in a private graduation ceremony near her Cambridge home.

"I'm really pleased for my mother," daughter Mary Fowler said. "As a child I wanted to be a physicist because it seemed to be so exciting. With both parents being physicists, physics and research was a normal topic of conversation across the kitchen table. " Advertisement

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