Science to 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). 

 Having completed my S2DS experience, I feel so thankful that I had the opportunity to participate in the 2021 cohort. Even though the course was virtual it felt so vivid and engaging. 
From the beginning to the end, the organizers have put such a great effort to plan a hands-on experience, involving exciting real-life data science projects, and made sure that the interaction between all members of the cohort is constantly up. 
S2DS is a great opportunity for anyone looking to dive deeper into data science and obtain work experience. It is hard work all the way through 5 intense weeks but also a fantastic networking hub. S2DS allows you to manage your own project within strict timeframes, and engage with customers in discussions and negotiations. The organizers' support throughout was phenomenal; from the set up of the working systems to the technicalities within the projects.     
I highly recommend this program. Above all, I recommend enjoying it! Take this opportunity to learn from each other and also teach others. You can always ask for career advice as S2DS has people with good experience in career coaching as well. Participate actively in all meetings, discussions to be able to make the most of it. S2DS has been a great step in my data science career.  

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. 

This is a wonderful course for PhD graduates in the Natural Sciences who are interested in transitioning into data science. The course was brief and tough, most of it was self-taught, which is perfectly suitable for fast learners with a PhD. The experience of communicating with the company and working in a team was especially valuable. Lots os additional technical help is provided, and there is a rich framework of talks and events that help the candidates navigate their ways into data science. I strongly recommend this course - it was a fantastic challenge and a really rewarding experience overall.

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.

 Dataknowledge Science may be a combination or mixture of mathematical and technical talent, which can need business vision additionally. These skills area unit won’t to predict the long run trend and analyzing the info.  Data science is a "concept to unify statistics, data analysis, machine learning, and their related methods" in order to "understand and analyze actual phenomena" with data. A data product is a technical asset that utilizes data as input, and processes that data to return algorithmically-generated results. For more Information please visit our website  https://www.gologica.com/course/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.