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 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 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.

The S2DS data science boot-camp was a great experience that allowed me to gain new skills, extend my professional network and learn more about data science careers. Highlights included:

Meeting new people with different backgrounds and ways of thinking, with a common interest in moving from academia to a career in data science
Connecting with the broader network of alumni, including panel evenings with course alumni in which one can learn about both finding jobs, but also the longer-term career paths that data scientists travel
Working alongside project partners from commercial companies, learning how to craft insights that solve real-world problems and offer value to potential clients
Technical mentorship that goes beyond just coding to offer valuable perspectives on the role of data science within organisations
Time and space to learn about new tools in data science and understand how the strengths and limitations of different quantitative methods relate to business needs
Experience working collaboratively using industry-relevant tools (GitHub, Slack, AWS) and Agile practices

This is primarily an intensive programme for working on a real-life data-science project - there is very little taught content, just some 1-2 hour seminars in the first week, and some YouTube tutorials to follow in preparation for the programme. However these are very good. The lack of taught content is why I gave 3 stars for "Curriculum" - it is not extensive, but it is good.
Don't do this programme though if you want a lot of formal grounding in programing or data-science. *DO* do this programme if you want to learn a wide range of very practical and broadly useful skills mostly through self-directed learning and the time/space to absolutely immerse yourself and do loads and loads of coding! I knew this was what the programme was about and it was exactly what I wanted. I was a lot more skilled in many areas after just 5 weeks. It would have taken several times as long to learn the same stuff as part of a job, assuming the opportunity was even there.Unsurprisingly for a "bootcamp" you need to give this absolutely your full attention for 5 weeks. Don't plan anything else during it! And you should realistically expect to be working rather more than 40 hours a week although the choice is yours. It is entirely on-line though so the fact you're working from home makes the longer hours very manageable.Also, be entirely prepared for the fact that you will encounter quite a range of technical problems many of which you will need to solve with very little help from others. This is both a plus and minus - I was surprised and proud of some of the stuff I actually managed to fix pretty much by myself simply because I had to. This was ultimately a good thing!I would highly recommend S2DS - it is very good value for money indeed, and the benefits go far beyond merely learning some machine-learning and more Python (for example, fantastic careers support, fantastic access to all kinds of recommended resources, experience working with/for an actual client, working on a team-project). 

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 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 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.

Allow me to give the take home message at the start - if you are from academics and want to get working experience of data science and industry, the five weeks at S2DS are of great value.

Given that we have a great deal of technical training in our academic career, it may look like doing data science is not very different. I believe this program will change your opinion like it changed mine.

Like most of the people who joined this program, I do have a strong academic background. I have completed my PhD in Physics, and then have been working as a post-doctoral fellow. My research work mostly involves programming, data analysis and problem solving. One year back, I decided to make a transition to a career in data science, motivated by desire to solve real-world problems. I learnt machine learning, deep learning, data science by taking various courses. 

It is hard to get a data scientist job without hands-on data science experience. Looking for opportunities to gain experience of working on industrial projects, I came to know about S2DS through a friend who had joined the S2DS program and successfully transitioned to a data scientist career.

The most important skill which I Iearnt in S2DS and which is very critical in industry is communication. I closely worked in a team of 4 people, all of us from different background, academically and culturally. It was great opportunity to learn and develop the communication skills. This also gives the opportunity where we are working together almost all the time, updating each other using slack, regularly meeting through hangout. In addition to the communication within the team, we were interacting with our clients twice a week, where we used to get clarifications on the problem we were trying to solve, and constantly updating them we the progress we were achieving. Working as a consultant was a totally new and fun experience. 

The mentors at S2DS were extremely supportive. We had regular scrum meeting with S2DS technical mentors, where we got clarity on the ideas we were following and also directions to pursue in order to solve the problem. S2DS also looks into whether we are happy as a team, making sure that team remains on a cohesive ground. 

To conclude, it was really a great experience for me. I got to learn a lot, be it the hard skills or the soft skills required to a successful data scientist. I will highly recommend this program.

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!

This 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.

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. :)

Pros:
During this program I worked in a team of four with a company in a commercial DS project.

It is a very intensive program with very interesting webminars, panel discussions and tecnhical support from internal and company mentors.
At the beginning I was reluctant about how to work remotely in this kind of projects (as I have done all learning/projects by my own in the past), but the countinuous meetings with the team and mentors made the communication and team work being a success.
Also, I tried something called 'pair-programming' that, at first, sounded like a very difficult thing to do when people are not sitting together, but after the experience, I am planning to add it to my way of working( it is a great way of moving fast in the project at the same time of learning from others).
Another thing to highlight is that S2DS community is great! Lots of activities are organised every week to know each other as we worked remotely and there was even a 'Day out' in London that was excellent!

I would recommend this course, without hesitation, to anyone struggling to move into data science or who wants to find out what it’s like to work as a data scientist.

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.

Pros: 

- 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

Cons:

- 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. 

I took part in the S2DS Virtual Program in October 2019 while based in Germany. The application process consists of an online application on the Pivigo website, followed by an interview which tests basic technical knowledge as well as asking general motivational questions. About 3 weeks before the program we were given access to several program-specific videos covering things such as programming in Python and R, data visualisation and git. The videos are very clear and great to get started with any topic you are unfamiliar with. Also provided is a document suggesting additional learning resources as well as interview preparation materials.

On starting the program there are several introductory talks for the first two or three days; however, the majority of the program (which lasts 5 weeks) consists of working on a real data science project from start to finish with a client. My team was working on an interesting project with the UK Food Standards Agency (FSA). On assigning projects, individual preferences are taken into account. The virtual format of the program works extremely well and is of great benefit, especially with regards to changes in working habits in recent times. The majority of the teams made use of Slack, Github, Zoom and Trello for communication and team organisation. The team is generally left to its own devices; however, regular meetings are organised with a company mentor (in our case from the FSA) and technical mentors from Pivigo to keep the team on track. Another nice touch is regular individual meetings with a personal mentor at Pivigo, where additional more private matters can be discussed. 

Aside from the project, virtual networking sessions as well as Q&A sessions are organised to get to know the whole cohort. These sessions are not only great for practicing networking skills but also make you realise that everybody is in the same situation. A series of panel debates are also organised regularly with data scientists from startups, corporations and former S2DS alumni. These are very informative and give a good overview of the breadth and depth of roles available in the world of data science as well as allowing you to ask any questions to the panel.

A very helpful careers presentation was also given towards the end of the program, which really made me think about changing my approach to the job search.  Immediately following the program individual CV and Linked In profile reviews can be requested at any time. You are also given access to the whole Pivigo S2DS alumni network (over 600). This is useful not only for access to privately shared job roles, but also as a knowledge resource for any technical challenges faced later in your career.

At the end of the program final team presentations are given in the form of a webinar in additional to the requirement of a written report for the client. Not only was there a great sense of achievement on completing the program but a real sense of community with the cohort. The program was an extremely useful topic in a recent interview I had: here the journey of doing a project from start to finish offered by S2DS was key. The interviewers were also impressed by how so much was achieved in just the space of 5 weeks. 

I can wholeheartedly recommend the program to anybody interested in a career in data science. I would emphasise that for an academic thinking of moving into industry (like me), the experience is invaluable even if you do not end up pursuing a pure data science role as a lot of the things learnt here are transferrable to many related technical roles.

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.