Science to Data Science

The Science to Data Science program offers an exceptional professional development experience. Working with highly talented (mostly PhDs) researchers from diverse international backgrounds, combined with unlimited access to technical and non-technical... Read More mentors, creates an environment of excellence. The professional program management and partnership with industry stakeholders provides real-world project experience rather than typical training. The program successfully replicates actual workplace dynamics through international team collaboration and stakeholder communication, making it an ideal transition into data science.

I recently completed the Science 2 Data Science Bootcamp, and I must say, it was an incredible experience. The 5-week duration was intense, but it allowed for deep immersion into the world of data science. The highlight of the bootcamp was undoubtedly... Read More the project-based approach. While, it might initially feel overwhelming one quickly gets accustomed to the expectations and this results in accelerated learning. This process accelerates learning in comparison to lectures and MOOCs. What truly stood out for me was the agile environment fostered by the bootcamp. My team of 6 data scientist collaborated seamlessly, mirroring industry practices. This not only improved our technical skills but also our ability to work effectively as part of a team. We worked closely with a client on delivering a computer vision ML tool, which was both challenging and rewarding. The real-world application of data science principles added a layer of authenticity to our learning. The technical mentors and the project coordinators were fantastic and always on hand to help us out through any difficulty. They always offer lifelong career support and help tailoring your CV and cover letters for various jobs. In summary, S2DS was a fantastic journey that provided hands-on experience, teamwork opportunities, and expert guidance. I would highly recommend it to anyone, specifically academics looking to transition into the world of data science and gain practical skills that are directly applicable in the real world.

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!