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.
Recent Science to Data Science Reviews: Rating 4.93
Recent Science to Data Science News
- Data Science
In PersonFull Time4 Weeks
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.
- Start Date
- None scheduled
- Class size
- Minimum Skill Level
- PhD Required
- Placement Test
- Data Science
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.
- Start Date
- None scheduled
- Class size
- Minimum Skill Level
- MSc, PhD
- Placement Test
Science to Data Science Reviews
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I was already employed as a software developer when I enrolled in S2DS, I had a PhD in astrophysics and I really wanted to exploit my skills into a data science job but at that time I had no idea about what was a data science job, how to move properly into this field, how to present yourself to the market such that your experience could be considered valuable by recruiters.
S2DS helped me in all of this things. Moreover I found a lot of cool and talented people, friends who inspired me and teach me a lot. When you have to learn things on books or by yourself sometimes it can take years, but when you have the luck to make a real experince and find someone who can teach you with passion, this can boost your learning.
When I arrived at S2DS I had no claim, I knew it wasn't a magic and that I had to work hard, then we struggled a lot to finish our project in time, we suffered together, we put in it all our effort but finally we were rewarded and this is why I remember so pleasantly this bootcamp.
Close to finishing my PhD in Mathematics, I was undecided whether I should pursue an academic career or not. Getting a job in data science seemed a reasonable alternative, but I had no experience in the field. After some research online, I found the 2017 S2DS Training in London, which seemed the perfect occasion for trying data science in a business environment before actually taking such a job.
The application procedure was pretty straight forward - we had to send a CV and a motivational letter and then had a very short Skype interview, in which we quickly had to explain one out of three proposed statistical/machine learning concepts. (I explained the Bayes theorem.)
The classes were held in the first one-and-a-half weeks of the programme and mainly focused on Soft Skills that people from academia lack. We had lessons about teamwork, business communication, marketing, strategy, economics etc. All teachers were excellent speakers and even though the talks stayed superficial (2-4 hours are not enough to dive deeply into a subject) they communicated essential concepts. There were also a few technical classes (Introduction to R/Machine learning in Python etc.), but these classes were not sufficient to properly cover those areas. (Again 2-4 hours are not enough to cover these subjects and going through some online-tutorials is probably more effective to start learning these things.) Although several people would have preferred more technical lessons, I personally found the classes interesting and well-designed. Since the participants had very different backgrounds (from theoretical mathematics and biology to machine learning and statistics) it would have been impossible to provide technical classes that were useful for everyone.
The main part of the training however consisted in working on a project with one of the partner companies. Based on our preferences, we were divided into teams and assigned to these projects. The topics ranged from finance and marketing to health care and even applications in education. The individual experience here of course depended on the team and the company - but the general impression I got was that the teams were thoughtfully composed and most companies offered good support. A company tutor, a technical tutor and a tutor from Pivigo (the company that runs S2DS) were available for questions and assistance. My own project consisted in predicting logistical effort for a larger company and I was very happy both with my team and the supervision by the company.
Althogether I am very glad that I participated and I recommend the programme without hesitation. In particular if you want to start working as a Data Scientist in London (or Great Britain in general) it might be a huge boost for you career also because of the contacts you make as a participant - several of my former fellows found jobs there. However, I seriously recommend to study some machine learning and statistics in advance in order to get most out of it.
I decided to enroll into the March 2017 Virtual S2DS programme, and I did not regret it! At that time I was finishing up my postdoc, and was ready to get some experience of what an industrial data science project feels like.
Pivigo split us into teams, and each team was assigned a client company, to whom we were essentially delivering a data science project, with specific deliverables. We followed the agile methodology, with frequent commits to our codebase, and regular updates and Q&A sessions with the client company.
My teammates were friendly, collaborative, keen to learn, and smart. The mentors and Pivigo staff were very supportive during the program, and mediated our conversations with the client company effectively.
Even though this was a remote programme, due to the constant communication via online tools such as Slack, Google Hangouts and others, the ride was smooth. Furthermore, Pivigo organized numerous Q&A sessions with experienced data scientists, which really helped in answering pressing questions about how to make it as a data scientist out there.
If you already have a grasp of statistics, machine learning and programming, this bootcamp will help you get practical experience working on a real-world project. Even though at times it was challenging, overall I found it very rewarding and worth the effort. highly recommend it!
When I started to plan what to do after my PhD in astrophysics, I got recommended the program by S2DS alumni. I attended the 2016 course in London and found it was a great help in boosting my confidence in my own data science abilities and working in a team. It also removed all of reservations I had about leaving academia and moving into the unknown.
I found the mentors and pivigo staff to be very valuable and supportive during the program. They were on hand all the time to help with technical support, career advice and general encouragement.
The lectures didn't increase my technical ability (online in-depth courses are better suited for this) but gave me insights into how to apply my existing skills.
Overall, I would highly recommend S2DS to anyone with existing technical skills looking to move into data science.
I decided to apply for the S2DS London programme as I was heading into the final year of my Astrophysics PhD. I was starting to think about leaving academia but found myself in the position of having no commercial experience to call upon for any job applications/interviews I may have in the future. I also had no way of really knowing what it was like to work outside of academia. This worried me, and when I heard about S2DS it immediately caught my attention as a potential solution to this problem.
Prior to S2DS, I had a reasonable amount of data analysis experience under my belt from my academic studies and would class myself as fairly competent at programming in python, but I had pretty much no clue about business and no formal "data science" training whatsoever. One of my concerns before attending was that I may have been out of my depth, however it turned out I didn't need to worry. The S2DS course is well organised to suit all levels of ability (within reason!) and with a little pre-course preparation on websites such as Udemy, Coursera, Codecademy etc. you will be up to speed in no time.
The course itself is in the form of one-and-a-half-ish weeks of lectures, across different business and programming topics, followed by 3 and a bit weeks of project work with your sponsor company. I found the lectures hugely informative, and actually very engaging and interesting compared with the average academic conference. The team at Pivigo have a lot of contacts, and chose some extremely good speakers.
Within a week of starting the course, you will meet your company sponsors, technical mentors and (hopefully!) be given all the data you need for your project. (I say hopefully because for my project, due to various issues it took a little while longer than that, but it was all good problem-solving experience). Then it really is all down to you and your team from there. My project was interesting, challenging, frustrating at times but overall I found it very rewarding. I came out of this experience realising that data science was a career path I would like to pursue, and S2DS has given me a valuable network of people to get me started.
The atmosphere on the course was extremely friendly, all the attendees were fantastic, along with the Pivigo staff. People came from all over the world, from all sorts of backgrounds, whether straight from a PhD or Postdoc, or from later on in life looking for a career change. The course really is for anybody looking to transition into the field of data science. The work is intense, but there is also plenty of time allowed for socialising, both through numerous planned events as well as spontaneous activities such as movie nights, golf or a trip down the pub. Networking is strongly encouraged, and you will get plenty of time for this, both with each other and with lots of companies, many who actively recruit directly from the S2DS cohort.
I already know people who attended the course who have gone on to take jobs directly as a result of this experience, however I cannot speak for myself yet as I am still in the final stages of my PhD. What I can say is that I have received a huge amount of interest from companies and recruiters on LinkedIn since adding my S2DS experience to my profile, so this course appears to have great value as a stepping-stone into a career into data science.
To anybody reading this and considering applying to S2DS, I can highly recommend it. Be prepared for some hard work and some frustrations - of course not everything related to the projects will go completely to plan - but overall it was a hugely enjoyable and informative learning experience, one which I genuinely feel has changed my life.
Being still undecided whether to leave academia or not, I decided to participate in the S2DS bootcamp to get a flavour of what the life of a Data Scientist in a commercial environment would be. And all I can say is that this has been a great decision.
I opted for the S2DS Virtiual course because, due to family constraints, I would have not been able to move for 5 weeks to London, although I was a bit skeptical about the online format. I knew people who participated in S2DS London in the previous years and they all enjoyed and reccommended it, but I was not sure the Virtual course would be as engaging as the residential one.Truth is that the team at Pivigo are really great at running the course and are able to make the online course feel almost as a residential one!
The format is really good and it makes you (or at least me) realise that as a PhD/Postdoc what one is missing for a successful as a commercial Data Scientist is not technical skills but the soft skills to adapt to a different way of working.
The projects are generally quite interesting and the company mentors are very engaged and helpful and the staff at Pivigo is extremely helpful and supportive.
Being part of the S2DS Alumni community is a great way to start a professional network of data scientists and industry experts, a crucial element when looking for your first data science job outside the academic world. Although the course (and the related network) is quite UK centric, including it in your CV will boost your value in the eyes of recruiters and companies all over Europe.
In conclusion, I would definitely reccommend S2DS to anyone looking to transition from Academia to a commercial Data Science role! It will help you a lot in jump-starting your new career and, most of all, you will truly enjoy it!
I had a previous experience as a Post-Doc in Bioinformatics and Systems Biology within the academic set-up before enrolling on the 'SCIENCE TO DATA SCIENCE VIRTUAL' . I was interested in gaining experience in the applicatrion of machine learning to 'BIG DATA'. S2DS provided me with an excellent opportunity to do just that in the sense that I had the opportunity of developing my machine learning skills on a project which involved analysis of large datasets provided by an online marketing company. Apart from the project I was able to interact meaningfully with the other would-be Data Scientists, and also gained experience of working in a remote virtual environment. I recommend the 'SCIENCE TO DATA SCIENCE VIRTUAL' to any aspiring Data Scientist who are unbale to go to S2DS in London.
Science 2 Data Science, offered my the necessary experience to help transition from academia to industry. I have a very theoretical PhD, and i wanted a platform to show off programming and data science skills that i had aquired through self teaching. S2DS London was this perfect platform!
I got to work for 5 weeks in an intensive but fun work environment with like minded collegues who all shared the same goal. What was extremely pleasing was that i loved the project i was assigned to. I was able to work for a start up and experience the perks of working within the start up culture and at the same time work on a fraud detection problem that would have a truly massive impact on the business going forward.
If you want to be part of an amazing network, gain some experience into working on a real life data science project which not only develops your technical skills skills but also the soft skills needed to work within a team in industry, then S2DS is the place to be.
From the day I decided to switch from academia to data science; this course was on the top of my list. I was originally planning to do the on site course but the on-line course tunred out to be the better option (no UK visa hassles for me). The course was intense but for a good reason and it gave me a sense of how data science roles are in real commercial settings. Our team of three, had great mentors both from course side and the company providing our porject. Although I already had quite good technical skills, the course thought me a lot. My biggest achievment was how to work in collabration with others toward the same goal in a short time frame. I also learned how to look at analytical problems from a commercial view point.
In the end, beside the great experince from the course itself, I had a better understanding of what a data science do and what type of roles I would be better to apply. Eventhough they mainly help for looking jobs in the UK, by just adding the course to my CV I were asked for more interviews outside of UK and I was able to show case my performance potential in a commercial settings.
I highly recommand this course for all the academics who are planning to pursue a data analytic career.
If you are in academia and wonder if data science would be a good or feasible idea for a new career (hint: it is), this is what you are looking for.
S2DS gave me the hands-on experience I needed to be confident about my potential and perspectives in data science. This is something that any MOOC or Kaggle competition would never be able to give you. I can't stress enough the importance of working in a team for a company whilst trying to solve a business problem. The value of such an experience is incommensurable, and you will take advantage of it on your CV and during job interviews.
S2DS also gives you access to a network of data scientists, career advice, and job leads, all extremely helpful for your future data science career.
All this comes in the form of quite an intense but very enjoyable 5-week experience, where you'll be in the company of very like-minded people. The opportunity to meet and discuss all sort of ideas with other participants is another invaluable perk.
I was quite suprised by how well the virtual program worked, despite having to work remotely with people from all corners of the continent. That definitely worked much better than I expected.
In short, if you are an academic looking to jump into the data science endeavour, S2DS is a no-brainer.
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!
My background is Astrophysics and I have worked in academic research for about 10 years from my undergraduate to my almost 5-year tenure as a postdoctoral researcher. At some point during my academic career, I started considering moving into data science as I found it interesting and because there are several aspects of academia that did not fit well with my working style. Then something happened: I got selected for the Virtual S2DS bootcamp.
It has been a week since my Virtual S2DS experience ended. Do you remember the satisfaction after finishing your master degree/PhD? I am feeling the same satisfaction, with the difference though that this time it is not my own personal achievement only. Instead this time the achievement is that of a team formed by individuals (including myself) who: i) get to know each other’s skills and characters; ii) acquire new skills; iii) learn to trust each other and to work together to solve a data science challenge for a client company; iv) commit to achieve previously set up goals; v) produce deliverables for the client company, with practical and direct impact on their decision making process. The bootcamp is like an experiment concentrated into five weeks during which the key tools to be able to successfully conclude it are teamwork, communication, problem solving, time management and analytical skills (e.g., coding, machine learning, mathematical modelling, statistics and data handling and visualisation). I believe that these tools are the fundamental requirements to become a successful data scientist.
So, if a career in data science is what you are thinking about, apply for this bootcamp and enjoy the journey. You will feel motivated, interested, stimulated, taken care of and guided by the Pivigo staff members and will have a lot of fun. Sometimes you may feel that the project you are working on is not going the way you expected/hoped to. However, it still a scientific project the one you will work on and, by definition, unexpected issues can arise. One has to be ready to face the unpredictable and solve the problems in a finite amount of time and by the given deadline. I believe that this aspect is a very important one to experience on your own skin, so that you will be ready to solve such hurdles later on during your prospective data science career within a business environment. In fact, differently from many academic environments, deadlines must me met and this aspect is a fundamental one to take into account.
Furthermore I have had the chance to meet extraordinary people from different countries, from completely different backgrounds than mine and sometimes with completely different characters. Being immersed in such a diverse environment is a great opportunity to grow into a better and more competent professional. I have made valuable colleagues and friends during this experience and I was able to learn a lot from them. I am still in touch with many of them. In addition, I have been given the chance to join a large network of data scientists, which is the perfect platform for job hunting.
Finally, Pivigo provides resources for training on many topics (e.g., machine learning, python and other programming languages, statistics, project management, business) before the starting of the bootcamp. I found this particularly useful as I had the chance to deepen and/or refresh and/or build up my knowledge on the topics needed for the project.
In a few words, I highly recommend this bootcamp and I wish you the best of luck for when you apply for it!
I recently completed the S2DS Virtual program, and like many of the other participants I was nervous at the beginning of it all because I didn’t think I’d be able to contribute much to the project based on what I knew going in (I was also nervous because of the time difference, but maintaining London hours in Indiana wasn’t as bad as I thought it would be). There were a couple of lectures in the first two weeks about Teamwork and How to Code Well, and for the virtual program we studied various data science topics independently before the official work started, but most of what I learned in S2DS came from actually working on my project with my teammates, and it was an invaluable experience.
I also thought that the ‘Virtual’ part of S2DS worked very well. Through Slack, Google Hangouts, daily group Zoom chats, and ‘speed networking’ with the other participants the S2DS program didn’t end up feeling very ‘virtual.’ The virtual component of the program also helped highlight the importance of communication in data science. Since all communication was done online, it was critical throughout the program to communicate ideas and results well and also to communicate openly and regularly with your teammates. I think working through a data science project with a company from start to finish online, helped us all further understand the importance of being able to explain the results of your project to people with different backgrounds and experience and also helped us improve on our communication skills.
In addition to the support from my teammates, there was also a fantastic support system from Pivigo throughout the program. Both the technical mentors and the community mentor were there as resources in addition to the company mentor for the project to help us out when we had difficulties. So although a great deal of the learning and progress on the project happened individually or with my teammates, we did have several people we could ask for help.
It was a fantastic feeling to have a finished project at the end of the S2DS program, that we can discuss as real data science experience in job interviews and that the company will move forward with and build upon. I truly believe that S2DS is the best way to learn data science, and more importantly learn how to be a data scientist as part of a team.
I finished my PhD in Climate Physics in 2012 and went industry for 4 years afterwards, working for both a large corporation and startup. I signed onto the virtual S2DS course as I knew that the world is rapidly undertaking a technological shift towards Data Science and now was the time to equip myself with the skills.
The 5 week course has not disappointed! Being paired with 3 other international people and working on a real world data science project throws you straight into the deep end of working in a fast pace and results driven environment. It is intense and challenging however the Pivigo team do provide an excellent support network of technical advisors to help you throughout the course.
The main skills you will pick up:
- data pre-processing
- predictive modelling
- failing fast
- working in an agile environment
I would thoroughly recommend this course for anyone wanting to making the switch to Data Science, whether straight from PhD for if you have experience in industry - probably the most fun I've had at 'work' in a long time!
I'm a week out of the S2DS Virtual program and after a bit more time to reflect, I still consider this an outstanding program and an excellent introduction to help transition from academics to industry. The three main outcomes of the program, for me at least, was:
1- confidence in my abilities in Data Science,
2- an industry project with a finished product that I can point to and say "me & my team, we built this."
3- meeting a large group of talented, motivated, teamwork-focused individuals in the same situtation and with the same feelings (and not just the participants on my project!). Having kept contact with many others after the program so far, I feel a small network of likeminded individuals already growing.
Another main feeling I felt throughout the program was that Pivago/S2DS really cared about, and put serious effort into, ensuring everyone in the program (and the industry partners) got the most value out of it. Little of the time felt wasted, and support during all aspects of the program were fantastic. Here's some of excellent ways they helped us
-Pre-Program Study Materials: A long list of youtube tutorials and on-line courses to start to develop skills in machine learning, statistics, programming, teamwork, business before the course even started.
-Daily Meetings with Technical Mentors: While my team discussed our work for the day (and yesterday's results), Technical mentors listened in and provided excellent guidance when we streered off-track. We were fully allowed to develop ideas and approaches ourselves, and they helped by answering our questions. Also were available for detailed meetings. Also listened to our concerns about the project, and talked with our Industry Partner to help solidfy requirements.
-Introductory Webinars: A few introductory webinars about the program, good-coding practices, how to be an effective team, all these helped get our teams and projects up and running super smoothly (e.g. none of us had used slack or github before, but are now experts!
-Focus on Teamwork: coaching on teamwork skills, training having effective daily meetings (and they were very effective. I'm bringing this approach back to my post-doc group!!) bi-weekly meetings with a Teamwork Mentor who counciled us through any team/personal problems. S2DS Staff really worked hard to keep us a well-oiled team.
-Tools for Effective Virtual Work: I was shocked at how easy the viritual aspect of the program was. With slack and other tools, working with the team, discussions, etc. were no more difficult than if we were in the same office. And of course, S2DS Staff stressed we all work together :)
-Networking & Friendships: We had evening discussion sessions with everyone in the group, we had scheduled individual discussions with each person, there was a meet-up day in London UK most of us attended. Even though many of us didn't work on the same project, it was easy to build friendships with some, and network with many more. A great group of people.
Maybe most importantly, it felt fantastic to work with a group of talented, teamwork-focused, motivated, interesting individuals all looking to learn and build together.
All around fantastic program. S2DS Staff have clearly put so much effort into making it great, I would highly recommend to anything.
Since I was interested in starting a new career in DS, I decided to enroll on October 2017 Virtual S2DS programme by Pivigo, and I am very happy with this experience.
S2DS is an immersive 5 weeks training that offers the opportunity to work on a real DS project for a real company with a team of aspiring data scientists. That is very helpful cause it gives a taste of what it really is a data scientist's job.
The projects are engaging as well as challenging, and put to a hard test students' analytical, critical and creative thinking skills. As for me, I learned tons of useful things by working on the project my team was assigned, primarily from my teammates.
Furthermore, the daily interaction with teammates, technical mentors and company mentors fosters the participants to improve their communication skills, which is fundamental in order to become a good data scientist.
The organization was impeccable. Pivigo mentors and all the staff were very supportive throughout the programme and help make the communication both within the team and between the team and the company mentor easier and effective.
The organisers also provided interesting panel debates with experienced data scientists as well as S2DS alumni to share their experiences and valuable advice about the transition to industry and discuss relevant DS topics.
Last but not least, the daily interaction with your teammates and other students through the online platforms used in the programme (Slack, Zoom, Hangouts) helps make new friends and start building a network of colleagues which will enrich you both personally and professionally.
To conclude, if you have a solid scientific background and are motivated to transition to data science, I can only highly recommend S2DS bootcamp!
The programme really helps you transition from academia to data science.
Both the technical and non-technical mentors are always available to help you with the training or post-training. The networking done during the training is really valuable.
I would really recommend it to anyone who wants to move into data science with a scientific background, even if you are not sure if you wanna quit academia!
This program is helpful in many ways. Firstly, it implemented with real-life projects from the companies interesting in Data Science, which helps the students to learn the commercial awareness in every aspect of their works. Secondly, closely working together boosted the skills to work as a team and improved many soft skills of students, i.e., communication, time management, and presentation skills. In addition, the mentors were very supportive and attentive through out the program. Thirdly, the S2DS program invited some experts and alumni who are currently working in the DS industrial to share their experience, and it was highly valuable to the students. Moreover, the daily interaction with other students in the five-week span initiated an important network for the people who want to start a new career in DS. Therefore, I highly recommend this program.
I clearly remember that good conversation with a very good friend of mine from the institute where I did my PhD, where he told me about a very interesting data science trainig programme he took offered by Pivigo in London. Actually, he came back to London after he finished the programme for working as a data scientist freelance in a very good project. That conversation and the fact that I had been for long time thinking about other professional paths apart from the classic academia, pushed me to enrol the S2DS 2017 programme at the campus. Since the very beginning I noticed that this experience was what actually I was looking for: the programme gave me the opportunity of explore what data science means, not only from the theoretical point of view but, more important, from the practical point of view. The programme brought the opportunity of knowing what working as a data scientist means,
what skill are required for being good data scientist, how to interact with people and to create your own network, what you can and can't do if you want to get a data science job. All this was supported by the extraordinary work of the Pivigo people, from whom I got support before starting the course, during the course and after the course. I strongly recommend this data science programme to all the people coming from/stack at the academia that want to know how is the daily life of a data scientist and what you can do for getting into it.
I find it hard to imagine how I would have got a job as a data scientist without attending S2DS.
The key element at the heart of the S2DS experience is the company project, and there could not be a better way to bridge the gap from “I’m an academic who can analyse data in python” to “I can tell you how to add value to your company with this data, and I know how to do it.” Interacting with the company and technical mentors gives you the support you need to bound up the learning curve towards fully functioning data scientist. I really enjoyed working on the project with my three team mates - we all shared a similar academic background and it was great to work with like-minded people starting their data science journey from the same point.
Another great thing about S2DS is how the program injects you into the heart of London’s data science landscape through networking events and panel debates. This is invaluable for understanding the different sorts of companies out there looking for data scientists, and gives a big kick start to growing your data science network.
I would thoroughly recommend this course to any PhDs out there looking to make the transition to data science.
Being a postdoc in astrophysics for now 4 years, having won a few prestigious fellowships and having publish some interesting results so far, I am on the good path to obtain a position in the near future. Plus, I really enjoy doing research, and I have the chance to be part of an amazing group. I therefore want to continue in academia, if possible. But as many of you know, even with an excellent CV, it is extremely difficult to get tenure.
To have other options in case my career in academia ends, I started looking around and to speak to some friends that went out of academia. 90% of them, mostly physicists and astrophysicists, took a job in Data Science. Most of them were excited by their new job and because I did not have time to study Data Science on the side of my work, I started to look around for an intensive training. I was able to speak to four people that did S2DS, they were all very happy with the training, so I decided to apply. Besides my friends' reviews, I was also attracted by the concept of S2DS, a little bit of theory at the beginning of the training to catch up on Data Science tools and to understand the philosophy of working in the private sector and then directly working for a company on a real project.
Two months before S2DS starts, they will ask you about which type of project you would like to do and in which type of company. A month before S2DS, you are assigned a project with teammates (2 to 3) and you have a chat with the mentor of the company you will be working for that will give you more details about the project. In my case the subject of the project was close to what I wanted.
The training starts with 1.5-ish weeks of teaching, were you learn the basics of good coding, you get some notions of marketing, economics, but you also learn some basic Data Science tools. All the classes are very general and it is not possible to get lost. As I am coding quite a lot for my work, and have 8 years of programming experience with Python (the Data Science language) I did not learn a lot during those classes, but I believe it is very good to keep everyone on track. Most of the speakers were excellent, the S2DS team is doing a great job at curating speakers.
Then you start working on the project with your teammates. The mentor of the company is there to help, but also S2DS provides you a technical mentor, specialized in Data Science, that is nearly every day on the campus. In the case of our team, the project was not clear at the beginning and was extremely challenging for newcomers in Data Science. We had to do some unsupervised Machine Learning on a big data set of unstructured texts to detect anomalies. This perhaps does not ring a bell for you if you are not into Data Science but this is probably one of the most difficult chapter of Data Science today. From what I understood, the mentor of the company was not familiar with this kind of analysis, and he could just give us a few Natural Language Processing tools available out there. He was not very organized and in the end, we did not have a lot of interaction with him. However, the technical mentor provided by S2DS helped us a lot. He was not an expert in this field either, but he was able to give us some really good advices and I believe that at the end we did a good project given the circumstances. We definitely learned plenty of very interesting tools, how to work in a team and how to move forward when you do not know where you are heading to, in a few words very similar to scientific research. I would say that this experience is rather personal, and I know that it was much better organised for other teams. I do not think that S2DS is responsible for that and it is more the company that did not do the part of its job.
During the 5 weeks, several networking events are organised, to meet the London Data Science scene, speak to former academics that transitioned to Data Science, and look for jobs. A career affair is organised at the end of the training.
Even if the project I worked on with my teammates were perhaps not the best, I learned a lot during those 5 weeks, I am confident know that I can become a Data Scientists if I want to do so, and I have learned some tools that I apply to my research now. S2DS is really a great program, very well organised, were you work a lot, but you also share very good moments with your team, other fellows. I can only strongly recommend this program if you want to switch to Data Science or even if you want to learn more about this exciting part of Statistics.
Upon completing my PhD I was looking to transition in the data science field. Athough I had self-taught a number of the requisite skills that were needed in addition to scientific training I already posessed, it was very hard to gain any commercial experience. The catch-22 of needing experience to get the experience of an internship of junior position was rather frustrating.
S2DS offered a great opportunity for me to tackle a real commercial project for a multi-national company. In addition, being able to interact, ask questions and get a better understanding of what a data science role is like in a company was very useful.
The technical mentors of the course were very generous with their time and knowlegde, giving valuable insight on the soft-skills as well as the technical, helping me to understand some of the challenges when working in a business setting and having to relay information/resuts to a non-technical audience.
It is a fantastic way to start working on a team basis and building your portfolio. Besides, you get access to extensive networking that gives you the opportunity to gain insights into different fields of the industry where Data Science techniques are applied. If you come straight out from academia, it is one of the best chances to know more about the world business.
S2DS provides is a very useful if compressed immersion into the data science world. About 2 of the 5 weeks of the project are mostly devoted to lectures of various kinds. While, given the time constraint, these cannot be expected to go into great depth, they are still useful to get an overview of various subjects, and of some of the existing tools. The core of the bootcamp consists in teamwork (3-5 people) on a "real" project provided by one of their sponsors. The project quality can vary a bit depending on the sponsor - I was very happy with mine. This is in my opinion the most useful part of the experience, as it gives an idea of what a data scientist job consits of, at least in a particular sector. The Pivigo team and host of mentors are always very friendly and available and for support. Being surrounded by about 100 people of various nationalities and backgrounds and with a similar interest is also very enjoyable. There are considerable opportunities for networking.
I had several years of experience as a Post-Doc and knew the academic job-landscape well, but when I decided to venture forth I realised that I really didn't know anything about how industry operates, nor how to sell my skills.
S2DS was hugely transformative in this, as the curriculum and applied project are designed to give you an understanding and experience of working in companies. I was impressed with the support network Pivigo put in place for the course: we had solid mentoring from the company we worked with, excellent advice from external technical mentors and dedicated Pivigo mentors who helped with the organisation and communication within teams. I feel I learned more about how to organise a fertile work environment in those 5 weeks than in some year-long projects I can think about...
On the technical skills side, would say that this is not the main brief of the course, and applicants should come with a good grounding in place. The suggestion for preparatory online courses were helpful for that.
Doing the course has equipped me with knowledge of how to market my skills and what to expect out there. Having tangible commercial experience has meant that potential employers and recruiters now have a completely different attitude to me than before S2DS.
After the program I quickly got into conversation with one of the companies and I'm about to sign my first employment contract outside academia with them. Pivigo continued to mentor me throughout this process.
So in conclusion, S2DS delivered exactly what it says and has worked for me! Plus, it's a lovely environment with great people.
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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.