Science to Data Science

There are many courses and learning materials available, but a real-world project for a client company is not easily found, especially for people from backgrounds that are not IT-related. It is possible to organize a team for a ML project, but it's not... Read More easy to find teammates that can work full-time for 5 weeks with synchronized working time. Many boot camps advertise their career services, but not many of them have connections with recruiters and really hook up participants with recruiters as S2DS does. They only offer CV & cover letter tips and checks, which is well done at S2DS. Learning by doing with teammates with the same goal and ambition is the best way of improving one's ML skills. The 5-weeks is intensive and at the same time really rewarding.

We worked for 5 weeks on a project with a startup in groups of 5. We had strong technical and job support mentoring throughout the program and weekly networking sessions and panels with senior data scientists. I had the opportunity to work in an industry... Read More I love, test my skills in a professional environment, and finish it with many meaningful connections.

The 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... Read More 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.

Science to Data Science is a 5 week data science program that will allow you to work on a data science problem for a company. I participated to the 2020 virtual edition. I worked in a group of 5 (typical size) and we were lucky enough to deal with the... Read More data science team of the company. They were really supportive and helped us a lot on the different approaches we could use for our project. Thus, we did not really have to deliver a final product but more a tool to achieve it which will be further developed by the data science team itself. We are expected to have experience in coding (mostly R and Python) since it is really intense and you will also need to learn on the side new skills during the project. Teamwork is mandatory to achieve the objectives/goals set by the company (intensive use of SCRUM, Slack, and video meetings). Thus, you need to be well organized. The results are fast, which is more rewarding than in academia. There were also interesting lectures, for instance on how to apply for data science jobs.
I recommend this course to anyone who wants to get experience working for a company in a safe environment (really good help from the S2DS team if there is any problem) and to transition from academia to data science.

I graduated from the S2DS August 2020 program. This year instead of being in-person, the London the program was virtual due to covid. Pivigo made a huge effort to make the experience work and it did. Amazing program, very satisfied with it. Totally worth... Read More the money. I fully recommend it to finishing phds and post-docs who want to take a taste of data-science without jumping into the unknown. Over 5 weeks we went from what-the-hell-am-I-going-to-deliver-to-the-company to actually building useful AI software.

Note that, the lectures provided are very limited, and mostly oriented to providing a new view on things (business, finance etc). The more prepared you are before the start the better. (e.g. online courses on AI etc). This is not a course to teach you AI or ML. It's and opportunity to apply what you know to the real world. Also, there are non-AI projects available that relate more around software development and more traditional statistics.

The companies offering projects were energy, education, law, retail, finance, transport etc. Sizes ranged from non-profit and start-ups to mid and large scale established companies. One amazing part of the experience was the presentations at the end of the program. Watching more than 10 different projects be presented gave me a very nice view of the data science landscape, and I know have clearer view of what is the kind of data science I am interested in doing.

When it comes to job support, our cohort was unfortunate as due to COVID, the virtual job fair at the end was cancelled due to lack of demand. However, we do receive job adverts from pivigo.

My take home message is do it. You won't regret the time spent, and after the program you will feel much more confident on how to present yourselves at an interview setting, as you will realise in practice the value you can provide to businesses. Be prepared for an intensive 5-weeks though. There won't be much time for other activities, but its fully worth it.

S2DS is a project based bootcamp that helps analytical PhDs gain commercial experience. Participants are divided into teams of 4-5 and assigned projects at companies (there is a wide range of projects to be involved with). There is constant technical... Read More mentoring from the course organisers and often the companies have previous experience with S2DS projects. There are also relevant lectures and lots of opportunities to network with other students in the cohort. This is an intense 5-week course where I learnt lots about "real-world" data science and made lots of connections. Coming from an academic background, it seems like a great choice to move on to the data science industry.

S2DS 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... Read More 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.

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... Read More 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!

This was a 5 week intensive data science course for  people coming from academia ( MSc/PhDs). You are expected to have some experience of scripting before hand (typically Python or R). The course makes the student familiar with the way of working in on... Read More 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 jump straight into it blind.

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... Read More 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 job advertisements that are announced in members-only Slack channels. There is an informative webinar conducted towards the end of the programme that helps you to structure your job search, starting from designing your resume to negotiating your salary.

Overall I'd recommend S2DS to anyone willing to transition from academia to industry as a data scientist. :)