Faculty

Pros: - Do a real world project. Build practical skills and something substantial to talk about in job applications/interviews. - Build professional network. Group of people who are happy to help and support each other, long after fellowship is complete.... Read More - Get crash course in data science. Get strong sense of what is important and worth learning. - Do intensive practice on giving presentations, culminating in 5 minute presentation during Demo Day. Cons: - Not guaranteed a job in end, (but you do have high chances of getting job directly out of it) - Given short duration of project, focus is more on getting something useful done, rather than applying best practices in software engineering / data engineering workflows. You should find other ways of supplementing your learning to avoid developing bad habits (contributing to open source is highly recommended!)

This program, designed to help PhD students and postdocs get data science or data engineering jobs in London, is fantastic. The program is coordinated by a data science consultancy company, the ASI. Since the overwhelming majority of employees of the... Read More company have PhDs themselves, they understand the transition process from both sides, and know how to explain things in a way that academics understand!

Broadly speaking:
Week 1: Companies from across London come in to network with students and present their projects
Weeks 2-7: Your time is split between training courses at the ASI, and working on your project at the company office
Week 8: You present your work in front of all of the companies involved with the program

Most students were offered a position with their project company following the fellowship. If you aren't, or you don't want the job, then the ASI will arrange interviews for you with a range of companies, and give you advice/training on the hiring process.

The two most helpful aspects of the program were:
1. The program exposes you to a wide range of companies/projects at small companies/start ups that I had never heard of before. I left with a much better understanding of the range of opportunities available to me.
2. The ASI staff are incredibly friendly and helpful, and always willing to have an informal chat with you about your project/your career plans. They are willing to continue to mentor you even after the fellowship ends, and I still go back occasionally for Friday drinks to catch up!

I cannot recommend the program enough!

I took part in the first fellowship run by ASI Data Science. It was really worthwhile.

Each batch is only 15-20 fellows, which makes the program very selective. The fellows are encouraged to learn together and share skill, which makes for a really cooperative... Read More atmosphere. There is a bit of teaching, but the main part of the program is a six week project. The project involves solving a real data science or data engineering problem for one of the program's partner companies. I spent three days a week working at the partner company's office during the project. This gave me an idea of how a workplace functions, as well as the chance to really integrate into the company's data science team. Ultimately, it lead to the company offering me a job at the end of the project.

If you are currently finishing a quantitative PhD and want to transition into data science, I can't recommend this program enough. It is the only free data science program in the London area (ASI Data Science funds the program by charging recruitment fees when fellows are hired). Judging from conversations that I have had with alumni of other data science bootcamps in London, this program also provides a much better educational experience than competitors.

The term "bridging the gap between X and Y" is certainly an overused one but the ASI fellowship did exactly this for me. Coming out of a PhD in statistical physics, I was constantly being told I would "have no problem finding a great job" but I found... Read More that the interview process, especially within the relatively new field that is data science, was in no way gauging the skills I had developed in my PhD or my ability to develop into a great data scientist but rather just testing my knowledge of various fairly rudimentary technologies (of which I had no knowledge because they are not commonly used in academia/physics).

8 weeks later and I was confidently presenting an actual data science project I had completed with a FTSE100 company in which I had demonstrated my ability to generate real financial value. Before the fellowship, I had all but given up on pursuing a career in data science and was beginning to think about other career options, eight weeks later I was reinvigorated and applying for data science positions with confidence (I also received multiple offers).

Everybody on my fellowship who decided they wished to stay in data science had a job within a month of finishing the program, and many of us have stayed in touch since, forming a socially and potentially professionally valuable network.

I cannot overstate how highly I recommend the ASI Data Science fellowship to anybody who wants to transition from quantitative academic research to data science but is finding that the recruitment process is failing to value their skill set.