Science to Data Science
Science to Data Science offers both a 5-week data science workshop in London training analytical PhDs, and a 5-week online data science course for both PhDs and MScs. Both courses teach the skills needed to transition from a scientific background to become a successful data scientist, such as Python, R, Hadoop, SQL, and machine learning. Fellows accepted into the program will learn via hands-on projects, and 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.
All applicants should have intermediate programming skills in a mainstream programming language, and a strong desire to change careers into a data science role. Applicants for the in-person class should have a PhD, or be in their final year of a PhD. Applicants for the online course need at least a MSc in an analytical science, and some programming experience.
All Science to Data Science students work on projects with real companies, and Science to Data Science will help connect students to job opportunities via networking events and membership to the Science to Data Science Alumni group.
Recent Science to Data Science Reviews: Rating 4.93
Recent Science to Data Science News
- Data Science
In PersonFull Time4 Weeks
Start Date None scheduled Cost $800 Class size 100 Location LondonThis 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 Placement Test No Interview Yes
- Data Science
Start Date None scheduled Cost N/A Class size N/A Location OnlineScience 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 Placement Test No Interview No
Science to Data Science Reviews
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- Mr- 11/23/2020Emanuel Blei • Mr • Graduate • Course: Science to Data Science Virtual • Campus: Online • Verified via LinkedInThe S2DS boot camp is probably quite different from other offerings. First of all, it is £800 for the virtual version compared to many thousands of dollars for their competitors. Secondly, you have to have at least a master's degree or better still a PhD. That makes sense since the course is aimed at staff working in academia or recent graduate/PhD-students that want to make the switch to data science in industry. The boot camp might also not be your thing if you need your 12 hours of beauty sleep each night, because you simply won't have the time.
You will be surprised to hear then that this boot camp does not teach that much in terms of the nitty-gritty of programming. To a large degree, you are expected to either have mastered that part already or to pick it up during the boot camp as and when the need arises. In fact, more than 95% of the time is spent on a single project with a commercial client, and the whole format of the boot camp is built around that.
What the boot camp does teach very effectively is how to use the skills acquired in academia in a commercial setting and you gain some extra skills that are not relevant in academia but vital in business. You learn how to connect and deal with clients and their requests, and how to translate their needs into a business case and then into a project. You work with 4 other teammates for most of the five weeks to deliver the project and it is very satisfying to see when all the effort comes together at the end and results in a product that does have an impact on the client's business.
The staff are very helpful and are on hand to help with technical questions during the project. During the course, you spend some time widening your horizon to a variety of data science topics and there are several talks by industry insiders on what their day-to-day work routine looks like. Towards the latter part of the course, the focus shifts to carreer-development, and this offer extends to beyond the end of the boot camp. Overall, I felt that the course was well thought out in the way it gives students that much needed commercial experience and the confidence that they are ready for the job market.
So, if you have already most of the statistical and coding skills to work in data science but you don't know how to break into the industry this might be your ticket.
- S2DS- 9/14/2020Arnaud • Graduate • Course: Science to Data Science Virtual • Campus: Online • Verified via LinkedInS2DS is a program that will provide you with your first hands on experience in data science. It consists of a 5 weeks project oriented bootcamp where analytical PhDs are brought together to work on data science and machine learning challenges for a real company. Teams of 4 or 5 members are followed by a technical mentor as well as a business mentor from the company they are working for. The program includes lectures and social events as well as networking events. This program was for me the best experience I could wish for to start a new career into data science.
- Exactly what I was looking for!- 12/9/2019Miaomiao Yu • PhD student • Graduate • Course: Science to Data Science Virtual • Campus: Online • Verified via LinkedIn
Science to Data Science is a great head start for anyone who has the technical and analytical skills to be a good data scientist, but lack the commercial and industrial work experience that companies look for in a candidate. The programme lasts for five weeks and involves working in a small team of three to four people on a project. The limited time meant that it was important to (1) have a good foundation in data science skills (coding language, grasp of statistics, familiarity with platforms like GitHub, etc.) and (2) manage both our and the client’s expectations on what can be delivered.
The learning curve at the start was steep: working remotely and in a team meant that communication was vital. This was quite different from my experiences in academia and learning to delegate felt awkward at the start. We also had many meetings with the company to clarify the concepts and project goals. As it turns out, one of the biggest hurdles was to understand what the client wanted, translate that into data science problems, and construct an appropriate and feasible plan that we can execute.
We did a lot of exploratory analysis on the datasets we were given for the first two weeks, and started refining our ideas by the second half of the programme. We also started working on an interaractive visualisation platform, improving on the existing data presentation method. The team dynamic varies from one team to another: we were lucky to have found what worked for us early on: splitting the project into smaller tasks and tackling them either individually or in pairs. We communicated any difficulties we faced and always operated as a team (i.e. no one was left behind/out of the loop and no one tried to 'run ahead'). With the help of our external mentor, the CEO of the company as well as the Pivigo team, we delivered products that were incredibly valuable to the company.
The programme also included web-seminars on job hunting, CVs, teamwork and panel debates from past alumni and people who work in freelance, corporations and start-ups. We also had a daily Q&A session where we discussed a topic in data science that interested us. These activities ensured that we had an all-round learning experience.
In all, the journey reaffirmed my passion for data science and was truly the best thing I could've done for my career at this stage. Highly, highly, highly recommend!
- An intense but fulfilling experience- 12/8/2019Mark • Graduate • Course: Science to Data Science Virtual • Campus: Online • Verified via LinkedInThis was a 5 week intensive data science course for people coming from academia ( MSc/PhDs). The course makes the student familiar with the way of working in on data science problems in non-academic sectors (e.g. commercial, government or charity) where the deliverables are very different to ones one might be used to in academia. Typically working in groups of 3-4 on a project assigned by a company, the course forces you to learn things quickly, whether it be new coding languages or new data analysis concepts and methods. Also you are encouraged to work to a more formal way or deligating tasks and working to targets (e.g. the SCRUM framework). This was a very steep learning curve for the first couple of weeks for someone who has been working in accademia for 10+ years. One has to abandon conceptions of learning everything about a topic or working on a project to perfection, and focus on delivering objectives efficiently and effectively. Group work is another really big focus of the course. regular communication is essential between the group members and with the company. The fact this course is virtual presents a bit of a challenge compared to a physical face-to-face interaction. However we were able to use the tools we had (zoom, skype, trello, slack) to ensure were worked together effectively. By the end of the course, we had learned a great deal, both in terms of technical and softer skills (group working, communication, remote working). The company we were assigned was very happy with our final product which was very fulfilling, in a way a academic work is not. The course also had some excellent lectures on hunting and applying for data science jobs and understanding what to expect in different sectors. I would highly recommend this course to anyone wishing to transition from academia to data science. It's a fantastic way to get experience and insight into the industry without having to make a leap in the dark.
- Debashis Sanyal • Graduate • Course: Science to Data Science Virtual • Campus: Online • Verified via LinkedIn
I took part in S2DS Virtual 2019 that concluded in November. Although I was a bit apprehensive about the virtual format before the start of the programme, I must say that I was rather pleased with my experience at the end of S2DS. The entire cohort is divided in to teams of 4 (or 3) at the beginning and given a real problem from one of their partner companies (depending on your preference) to solve. Each team is assigned a company mentor and a technical mentor from Pivigo.
A large part of the programme hinges on effective teamwork. The team members stay connected all the time through Slack, Zoom, Hangouts, Skype, etc. We also had daily scrums in the mornings that were very useful to take stock of the situation and plan for the day. I realised that for succeeding in these kind of projects, it is important that each team member plays to their strength. The problem statement given by our client was not really well-defined, which probably mimics a real-life scenario in a company. This is where the experience during your PhD will come in handy. After numerous brainstorming and pair-programming sessions, we came up with a finished product in 5 weeks that was presented to of the full cohort, the Pivigo team, and the company mentors.
The Pivigo team organises a few webinars during the first week on good coding practices, teamwork, etc. but there are no technical presentations or teaching involved. During the course of your project, you'll have regular meetings with the client to discuss progress, and the technical mentor is always ready to help if you get stuck really bad. Apart from that, around 3-4 panel debates were organised where a number of data scientists were invited to talk about their journey and career path. There was also a daily Q&A session where the full cohort used to discuss a topic related to AI/data science, led by one participant per day. You'lI get to learn not only about different interesting topics, but also about your fellow participants. Another nice touch was the one-to-one speed-networking sessions where you get to meet each of your fellow participants. Having said that, the peer-networking is one aspect where the on-site programme clearly wins over the virtual one. I must add that all the Pivigo staff members are very helpful, and they really try to ensure that each participant is having a good experience during these 5 weeks.
Please note that this is not a traditional 'bootcamp' where you are first taught a topic and then handed out a set of exercises to test your knowledge. This is for people who are already comfortable with coding (Python/R) and have basic understanding of statistics and machine learning concepts. The rest you have pick up on the go. S2DS adds to your CV the commercial experience as a data science consultant which makes you ready to hit the job market. Another important point is that S2DS is a full-time commitment, you need to spend at least 8 hours everyday working on your team project.
Regarding job assistance, it is too early for me to comment since I've just started applying for jobs. But you do get access to the huge S2DS alumni network and the job advertisements that are announced in members-only groups. There is an informative webinar conducted towards the end of the programme that helps you structure your job search, starting from designing your résumé to negotiating your salary. Their UK network is quite strong.
Overall I'd recommend S2DS to anyone willing to transition from academia to industry as a data scientist. :)
- Remote program review- 12/2/2019Alex • Applicant • Course: Science to Data Science Virtual • Campus: Online • Verified via LinkedIn
I participated in the Oct 2019 Virtual Programme, which invovled a 5-week project in a team of 4.
This was good experience to work on a client problem as a team and to provide business value by utilising data science and machine learning methods.
Note that this programme is for those who already have a good grasp on the fundamental data science and machine learning methods. You will be expected to apply these skills to your client's problem in a team environment.
- Working in a team. A lot of my data science work involved working alone. It is very common to work in a team in industry, so having this experience is extremely valuable. I.e. using Git, Slack, Zoom calls, etc. to communicate and share work.
- Working remotely. This is extremely good experience in itself.
- Working on a real client problem.
- Really good for CV and interviews.
- Friendly team and mentors
- Sometimes not enough support with the project (e.g. mostly have to solve client problem in your team alone)
- Too many meetings that could be seen as unnecesary.
- The virtual programme innevitably makes networking more difficult as interacting with everyone on the programme is limited.
As for job assistance, there is really good advice from industry experts and technical mentors within the programme. Perhaps some more details about the general data science interview process would be helpful. Ultimately, getting a job will be dependent on the applicant's own efforts.
For those looking for good experience and something to write in your CV. This is a good programme. Extremely helpful to be able to speak about a client project and the value that you have given to their business.
- Roberta • Graduate • Course: Science to Data Science London • Campus: London • Verified via LinkedIn
I attended S2DS London 2019 and I absolutely loved it! Before starting, I was already sure I wanted to switch from academia to data science, and I was mostly looking for an opportunity to gain business experience before starting looking for an industry job, and to get to know other PhD students who were ready to do the same. In my opinion, there are three things that make s2ds a really great experience:
- The people. The cohort was full of international, like-minded, and very smart people, it was just very fun to be together all the time, from cooking together in the evenings to attending lectures and events. Moreover, the Pivigo team is super friendly, really helpful with anything you need, and very passionate about their jobs.
- The projects were well-thought, diverse, and really interesting! Spanning from finance to marketing to helping governamental agencies to detecting fires to recommending systems, it was really nice to get an overview of all projects during the mid-term presentations. I really enjoyed the diversity and the discussions that the presentations sparked.
- In the evenings, there were plenty of debates, talks, etc, which are a great way to get to know s2ds alumni, and, more in general, more experienced data scientist. These events were crucial for me to understand the pros and cons of moving to industry, common misconceptions about data science, and to see how quickly DS career paths evolve.
Additionally, I felt that s2ds helped my job search quite a lot. Of course I cannot know what would have happened without, but all the interviewers I talked to were really interested in the project I did (much more than in my phd work... :) ) and I could prove that I had at least a bit of feeling for the business side of projects (which is a very crucial point that many PhDs seem to underestimate..). I still got some rejections because 'you don't have enough business experience', but I think it helped in other cases (and I have a job now :)).
Hope you don't miss this chance and apply soon!
- Perfect Data Science Tryout- 11/21/2017Jonathan Gantner • Graduate • Course: Science to Data Science London • Campus: London • Verified via LinkedIn
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.
- Orfeas Kypris • Research Scientist • Applicant • Course: Science to Data Science Virtual • Campus: Online • Verified via LinkedIn
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!
- Stew Buchan • Graduate • Course: Science to Data Science London • Campus: London • Verified via LinkedIn
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.
- Rob Spence • Astrophysics PhD Researcher • Student • Course: Science to Data Science London • Campus: London • Verified via LinkedIn
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.
- Alberto Guffanti • Senior Developer • Applicant • Course: Science to Data Science Virtual • Campus: Online • Verified via GitHub
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!