Science 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.

Xavier Dumusque

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.

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. 

 

I had been waiting to move into Data Science from Academia for over a year and S2DS gave me the start I needed. I couldn't wait for the live corse so did the virtual one and recommend it if you are in the same position. You have to be motivated but the instrutors on the course do their very best to help with multiple checkins and are always on hand to give advise. I got to meet every person on the course with arranged one-to-one chats so I didn't feel like I was doing this alone. 

After the course I used the project I did and what I learnt from it in interviews to help me get a job. It gace the interviewers something to ask about and me something to talk about to show I understood not only data science but industry. And when it came to getting a second job, I went through the S2DS alumni groups and got a fantastic job thorugh their carrers area. 

Definitely worth the effort and time to take this program if you are interested in a carrer in data science. It's a great gateway in. 

I would recommend S2DS to any scientist interested in a transition to the data science industry. As other reviewers said, the skills are already there, but it's difficult to break through in the industry with only academic positions in the CV and without knowing anyone. S2DS fills both of that gaps, giving the vital commercial experience that is nowadays needed also for entry job levels, and a network to be able to put your foot in the door.
There is a huge variety of companies at the program, and most of them are hiring (and not only the people doing a project for them, I'm proof of that!). The project will give you something interesting to talk about in any interview you will have to do, while the Pivigo recruiters will help with your CVs and applications. I've got positive experiences with very few recruiters, and David at Pivigo is one of them.

Only downside, if I have to find one: the program is very UK (particularly London) oriented, so if you want a job somewhere else, the network will be less useful. But, again, I found a job in Paris through the Pivigo network, so staying in London is not an obligation.

The 2014 S2DS London summer school provided me with the perfect support and opportunities to transition to data science after leaving astrophysics. I wasn't sure exactly what I wanted to do, but the 'try-before-you-buy' approach allowed me to test out the business world for a month before making my final decision - I loved the company I did my internship with and they wanted to hire me, so I've been happily working there for 2.5yrs now.

The course itself was excellent and included a mix of training material and hands on project experience. It's 5 weeks of very hard work, but totally worth it, and the most energised I'd felt towards project work for a long time.

Besides the data science and business skills/training that the course includes, it also provides you with a ready made social/business network. Many of us have jobs in London and still meet up regularly. 

In summary, highly reccomend it for anyone looking to move from academia to data science. 

 

 

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.

I am really grateful that I had an opportunity to be part of this five-week data boot camp. I especially appreciated that it is tailored for people from academia who want to shift to data science. In this way I could meet other people in similar situations as mine, which was really helpful. Five weeks might seem short, but the program is so well organized that you learn many things during the short time. Probably the best thing is that you participate in a real-life data science project with a company. While working on this project, you can learn so many things, including (but not limited to) technical skills like GitHub or anything connected to coding and machine learning, using different tools working in a team, communicating with your team and with the client, regularly presenting your work in a clear manner, working in a fast-paced environment... I would be happy to recommend this school to anyone who is seriously considering switching from academia to data science.

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