Science to Data Science
Science to Data Science offers both a 5-week workshop in London training analytical PhDs, and a 5-week online course for both PhDs and MScs. Both courses teach the skills needed to transition from a scientific background and become a successful data scientist. Fellows accepted into the program will learn via practical tasks, and students will work in groups to complete real-life big data problems. The curriculum also includes 30 hours of lectures on topics such as professional development and business skills, designed to help students achieve their goals after the program. For those attending the campus-based program, housing in London is included. Applicants should have a PhD or MSc in an analytical science, some programming experience, and a strong desire to change careers into a data science role.
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Recent Science to Data Science Reviews: Rating 5.0
Science to Data Science London
This five week workshop trains analytical PhDs and scientists in the commercial tools and techniques needed to be hired into data science roles. The aim of the workshop is to create a pipeline of high quality, commercial data science talent.
- Minimum Skill Level
- PhD Required
Science to Data Science Virtual
Science to Data Science is a five-week online workshop that trains analytical PhDs and MScs in the skills needed to become Data Scientists. Fellows accepted into the program will learn through practical work, and teams will work in groups to complete real-life big data problems. The curriculum also includes 30 hours of lectures on topics such as professional development and business skills. Applicants should have a PhD or MSc in an analytical science, some programming experience, and a strong desire to change careers into a data science role.
- Minimum Skill Level
- MSc, PhD
Science to Data Science Reviews
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Without a doubt the best career decision I've made. I wouldn't be in my current data scientist position without it!
The S2DS London programme was such a great experience. Although at first it can feel quite daunting, you'll quickly find out that everyone is like-minded and supportive so it never felt like a competition.
I had initially expected formal lectures on the technical aspects of data science learning about algorithms and new technologies so I was surprised when so many of the lectures were so soft skills focussed. However, through several networking events and my experience on the job market I realised just how much more useful it was to have the lectures that we did.
S2DS gave me a much broader perspective of the data science landscape and made me realise that I already had the skills to be a data scientist, I just didn't know it before. Importantly it gave me a huge data science network and the relevant commercial experience to get my data science career started.
Even after the course, the assistance and advice I received was great. No recruiter I spoke to was ever that helpful or suggested I should aim as high as the team at Pivigo. Ultimately it was a presentation that I gave about my S2DS project at a Pivigo organised Meetup that got me the job I'm in now.
If you're thinking of starting a career in data science and are not sure where to start then I whole-heartedly recommend S2DS!
Before finding out about S2DS I had already decided that I wanted to move into data science, so before applying I had been working my way through a few online courses, as a way to get a broader understanding of the skills needed in data science. And worked on a couple of personal projects based on public datasets, just to get a feel for using some of the tools and an understanding of what public datasets look like. On the programme there was a good mix of people with different skill sets, which was helpful when getting stuck with a problem, as there was someone who was able to give me an idea of how to fix it or another way of looking at it.
I was one of the people that took part in the very first virtual programme, meaning it was a new experience for all involved, so there was no set rules about the way to work, we had to experiment and find what worked best for us. The way they do things now may have changed slightly, as they refined the programme, but the main way of working (conference video calls with all members and group skype sessions) should be the same. The members of my team were only a short distance from London (purely by chance), but all the other teams had their members spread all across Europe.
Doing the virtual programme didn't hinder my learning experience, because a lot of the learning involved going off and exploring how to do something. Then afterwards reporting back to the team, by sharing links to examples, useful info or some graphs that you had produced. And if we needed to talk, we just started a skype call or google hangout. I had done remote working as part of my PhD, so was happy with the idea of it, and I had been thinking about doing a data science programme for a while and when I saw the first S2DS virtual advertised in their newsletter, I was very keen to give it a try.
I was happy I gave the programme a try, as it gave me a better understanding of the business side of data science and helped me make a number of useful contacts. And I notice that after putting S2DS on my CV, I was getting through to more job interviews and it was something that a number of interviewers were interested in talking about. So it was helpful as a real world example, that I could use to explain how I had tackled challenges, met key goals and interesting facts I found out. And the people from S2DS continue to be very helpful after the programme, with advice and informing me about job events.
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You don’t have to be a data scientist to read into these statistics: A McKinsey Global Institute report estimates that by 2018 the US could be facing a shortage of more than 140,000 data scientists. The field of data science is growing, and with it so does the demand for qualified data scientists. Sounds like a good time to pursue data science, right? No kidding! Data scientists make an average national salary of $118,000. If you’re looking to break into data science, or just trying to refresh and hone the skills you already have, Course Report has you covered. Check out this comprehensive list of the best data science bootcamps and programs in the U.S. and Europe for technologies like Hadoop, R, and Python.
(updated August 2016)Continue Reading →
If you have a quantitative PhD and want to transition from academia to Data Science, you have a surprising number of options. One such option is Science to Data Science (S2DS), a 6-week fellowship in London to train students in the commercial tools and techniques needed to be hired into data science roles. Adam Hill participated in the first S2DS cohort and talks to us about his experience, working on a group project with a real company, and what's next!
How did you find out about Science to Data Science?
Kim, who’s one of the founders of S2DS, is a former astronomer and she posted in the Facebook group Jobs for Astronomers saying she was designing a European data science program and was trying to get a feeling for interest. At the time, I was working at Stanford and I had a couple friends go through the Insight Data Science program and having met people working at tech start-ups I started researching about data science and the transition from academia into the sector.
What did you do your PhD in?
I did my PhD in astrophysics. It was awarded in 2006 and I’ve done a few post-doctoral fellowships in a couple of different institutes. Most recently I won a fellowship that took me to Stanford for two years which included a one-year “return phase” to Europe to bring back the knowledge and skills I developed at Stanford.
When Kim mentioned the S2DS program was running this summer, it was nice timing for me. I’m looking at potentially leaving academia now that my fellowship is ending, so that was my motivation. I applied to the program and was lucky enough to be selected.
Had you considered data science as a career before you heard about S2DS and Insight? What made you want to make that switch out of academia?
The term data science probably didn’t even exist 5 years ago, but the idea of moving into the tech sector and working with analytical skills was always there. There’s always something of a clock running on an academic career, with the struggle for funding and the shortage of permanent jobs. One of my friends took a data scientist job with Twitter straight out of his PhD, which sparked my interest. I took the Stanford machine learning class on Coursera, which inspired reading books and doing more Coursera online classes. I realized that data science is a broad term and that my day to day research as an observational astronomer is processing data, looking for signals and trends. Hence, to a degree, I was a data scientist; it’s just the data I was working with is in astrophysics.
Did you apply to Insight in the US?
I just applied and have been invited to interview for the January program. Knowing friends who had completed the Insight Data Science Fellowship, I was attracted to their 100% placement rate, and they have a slightly different focus and style to what the S2DS program was doing which I think would be a good complement to my S2DS experience. Insight would give me access to a different employment market - I’d be put directly into contact with people in Silicon Valley and in the US.
At S2DS, did you have to make a guarantee that you would exit academia and take a job?
To a degree: you have to make a serious commitment to the program as you have to be on site full time for the full 5 weeks but you aren’t obliged to accept any of the job offers that might arise as a consequence of participating in S2DS. The majority of people/companies that we met during the programme were the project sponsoring companies although there were several additional companies that introduced themselves in one-off visits. At the end of the program there was a small careers fair attended by a subset of the sponsoring companies but there are ongoing job opportunities being sent to the S2DS participants through Kim and Pivigo Recruitment.
How many people were in your cohort and was there a visa requirement?
It was a class of 84 and we worked on projects in teams of 3-4 with a project mentor from one of the sponsorship companies.
Also, as far as I know, other than a right to be there for the S2DS program, there were no restrictions on you needing to have a work permit/visa to attend the program. S2DS is partnered with Pivigo Recruitment and their primary market is the UK but with the S2DS program I felt that they were looking for the best people to attend and so they didn’t make a selection in advance that you had to have a right to work in the UK.
What was the application process like for S2DS?
It was an online application - I submitted my C.V., answered a few questions about why I was interested, and about my background.
If they liked the look of your profile then there were individual one-on-one interviews with Kim. That was a half-hour phone call where she asked a few questions about technical topics like Monte-Carlo techniques and Bayesian inference to make sure that you’ve got some kind of grounding in statistical or analytical tools.
Did you have a background with Python or another programming language before you started?
Yes, I’ve been coding almost daily in Python for about 6 years now. It’s the primary language I use for doing my research analysis. Because I’ve been researching and looking to transition into data science, I’ve done a couple of online classes in other languages, such as R, just to introduce myself to them. In academic research, if you can’t cope with programming, you generally can’t do your research because the analysis software you want probably doesn’t exist.
How were the 6 weeks organized?
The first week was entirely lecture-based. We had practical problems in classes where appropriate but a lot of it was lectures. We got overviews of practical topics like SQL and machine learning and then there might be some interactive problems to play with.
Following that, we were broken into groups of 3 or 4 and each team had a project mentor from one of the 23 companies sponsoring the program. For example, the primary corporate sponsor was KPMG and they provided mentors to support four different S2DS project teams.
Did you feel like everyone was very qualified and able to contribute to the teams?
Yes, generally speaking. I was aware that I was probably one of the more experienced research people there. A lot of the people had just finished, or were about to finish, their PhD, whereas I’ve had several research positions after my PhD. As a result I came to the program with a bit more research experience than some of the others. But it was a nice environment with a lot of very intelligent people from different backgrounds and with different skill sets. A lot of us would chat over coffee and everybody was generally happy to offer ideas or insights on challenges people were tackling: “Try and use this library and this coding language; that’ll fix that problem for you.” Everybody there was very much on top of their game.
Did you find that there was a good amount of diversity in your cohort? Was there a good gender breakdown?
Yeah, it was really inclusive. I think about a third of the participants were female, which is higher than you might typically find in the STEM subject areas.
50% of us were either physicists or astrophysicists and there were quite a few biomedical people as well. And then there were some neuroscientists and at least one economist. There was a diverse selection of backgrounds.
Do you pay tuition with that program?
We were required to pay £360. It was very good because we also got accommodation provided for the 5 weeks in London. We were living in the student dorms and were doing our daily work at a campus in north London so we were effectively living next door.
We had to cover our own travel expenses in and around London, getting to the program, and food but other than that, £360 gave us access to the program and to accommodation. I know that that was one of the key things that Kim and Jason wanted with S2DS. When they looked around at other similar programs being set up in Europe, there was no guaranteed accommodation once you got in, it was up to individual participants to find somewhere to stay for the duration. One of the key things they wanted to do to really allow it to be international; was to make that promise that accommodation would be included in the program.
Did you notice that there were a lot of international fellows?
One of the guys on my team actually was an American. He’s currently an astronomer working in Chile. There were a couple of Americans, there was at least one Canadian. There were a lot of people from across Europe as well.
During that lecture-based first week, did you feel like you were learning new skills during that time or was it more like a refresher?
For me a lot of the technical skills it felt like a refresher but that’s because I’d been immersing myself in data science skills in the previous 18 months and trying to develop them. A lot of the information they taught was stuff that I’d done online in a 6 or 8-week online course. But then there were business lectures on subjects like marketing and economics which were completely new to me.
Whereas for some of the other fellows, a lot more of it was brand new for them. There was always something everybody knew to a degree. S2DS didn’t expect anybody to come fully formed but they gave a full set of lectures for everything so that we would all be well rounded by the end of the program and have “launch pad” for stepping into a new area of data science in which we had little or no experience..
I have to admit though; I still learned something in all of the lectures. I went into the Python lecture thinking I knew Python pretty well and there was one thing that was shown blew my mind and would make my life so much easier in specific tasks!
The lectures tended to be 2 or 3 hours of material generally. We also had a number of business-focused lectures on topics like balance sheets and marketing; general business concepts that would be brand new pretty much to everybody in that room because of their academic background. They explained the jargon and vocabulary that would allow us to talk to business people in the corporate sector.
Tell us about the projects you did with sponsor companies.
I worked with a tech startup called Shopitize. They have an app that allows you to get discounts on your grocery shopping, independent of the store you shop in.
They’re hoping to start finding patterns/trends/demographics in shopping behaviour; information they can then sell back to the brands to improve their advertising and target markets.
I worked in a team of three. We had one overarching goal which was to create our own look-a-like engine. We were meant to find out, through looking at the grocery shopping behavior that had been recorded by Shopitize and the Facebook profiles of their users, if we could predict which Shopitize users would buy certain products based on their Facebook profile.
We had general Facebook information on our users such as the movies that our users liked, the computer games, the books that they read, the music that they liked. Fun Fact: the number one movie among all of our Shopitize users is Dirty Dancing. And interestingly enough, the 12th most popular movie is 50 Shades of Gray and it’s not even out yet.
We found that the data we had doesn’t predict what users will buy in a supermarket which to a large degree we weren’t surprised by, but now we could prove it. And Shopitize was really happy with that result because there’s no point in paying for targeted marketing based upon these characteristics. There were also questions raised as a consequence of not finding a pattern. Is there still a pattern potentially hidden in here but we don’t have the data labeled or constructed in a way to see what the links are? It was really interesting to see what you could find when you linked different datasets together.
Was the project interesting to you coming from a very academic background?
Yes, it was. One of the really interesting things was the approach, that the company advised the team to take, in trying to break down the data; we formulated hypotheses, tested, compared results, validated the approach - it was all very scientific. Shopitize was great in the sense that wanted us to work scientifically. They wanted numbers and hypotheses; they wanted things to be verified and checked.
How hands-on were the Shopatize mentors?
It was advertised that we would get ~6 hours of project-specific mentoring per week. Our mentor we had was the Head of Architecture and Development at Shopitize. He met us on campus a couple of times and we would meet over Skype for regular updates or if we needed assistance. He was online if we needed to drop him an email. He generally had a debrief with us each day or every other day to see how things were going. We were using a private Trello bulletin board as well so he could see what things we were working on. Plus, everything we ran was on their servers to protect the privacy of their users and that it was their commercial data.
Then we went to the company on a couple of occasions. We went for a general meeting in one week to see where the company was set up and got introduced to the other members of the team. We went back towards the end of week 4 to have a dry run of our presentation to the company directors so they got a feel for our results and to give us feedback before our final presentation.
Was Shopitize hiring? Did you feel like they were trying to recruit your team?
They were one of the companies that showed up at the careers fair. Shopitize intends to hire at some point in the new year. When they signed up to S2DS, they were hoping to take on additional data scientists but the positions won’t be available until 2015.
So some of the sponsor companies did not come to the career fair?
That’s true, there was a subset of the project sponsors that came to the campus careers fair. But we had also had networking opportunities at different events including the Graduation Dinner where, in my experience, employers were encouraging me to contact them after the program ended. And now that the program has ended Kim and Jason, through Pivigo Recruitment, have been advertising data science opportunities to all of S2DS participants from both new companies and those that sponsored S2DS. I think it’s very much on ongoing process and there is a lot of support from S2DS to find a position for any participant who is looking for a new role.
Did you get a sense that your classmates were getting hired?
It certainly seems so, now after the fact three months later, I know that people have been hired into companies. Some of them have been picked up directly by their project sponsors, others have found positions with help from S2DS and some have just gone and applied for positions independently. I think of the 84 of us around a third are now in data science related roles; and that doesn’t account for the fact that not everyone was available to start away in the couple of months after the program.
Were you promised that you would be hired by the end of the program?
No, there was no promise of a job just a commitment to make us more rounded data scientists who would have a practical data science project under our belt that would put us in a stronger position when approaching companies who were hiring. I don’t think you can “build” a data scientist from scratch in 5 weeks but you can take analytical PhDs who have a lot of the skills already and give them experience in a commercial data science context that make them more attractive to employers.
But as an example of this as a direct result of participating in S2DS I’ve had interviews in the past few weeks with a number of companies, including Facebook, for a role as a data scientist.
What are you up to now?
I’m still finishing up my fellowship and committed to working at my university for another couple months. As I said, I put in an application for the January Insight program. Otherwise, I’ve got a number of active applications through Pivigo.
What do you hope to get from Insight that you didn’t get from S2DS?
It’s encouraging to know that Insight has a 100% placement rate but, for me, it’s about getting more practical data science experience and being given the opportunity to network and build connections with some of the leading tech-companies. If there was only one place with a demand for data scientists it would be Silicon Valley and Insight gives me the opportunity to meet people there; it expands the pool of employers from which I can find the best role that suits me. I really like that it’s project oriented like S2DS and I like the idea of the small cohort size and the one-on-one mentoring. It’s a different approach to S2DS and I actually think the programs complement each other nicely.
Would you recommend S2DS to someone else?
I would highly recommend it. I know that Kim, Jason, and now Maya, are actively developing the S2DS program and trying to make it even better next year. Kim and Jason really wanted feedback from us (the participants) and I think from the sponsoring companies too, to make sure that the program delivers what everyone wants and to continually improve what is offered. It’ll be really interesting to see what changes they make into next year’s program.
One of the best things about S2DS was the minimal cost to the fellow, it really makes it open to people based upon the quality of the candidate. Arranging for accommodation in London really did make life a lot easier. I’ve mentioned S2DS already to a number of PhD students and post-doctoral researchers that I work with. If they’re planning on leaving academia, then next summer, they should be looking at doing the next S2DS class.