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Courses: ASI Data Science & Engineering Fellowship



About Faculty

Faculty (formerly ASI Fellowship) is a highly selective 8 week programme in data science and data engineering for outstanding PhD graduates and post-doctorates from UK, Europe and US top universities.

Since 2014, we have run eight Fellowships with 120+... Read More

The programme is completely free for Fellows. Fellows receive specialist technical skills and soft skills training, whilst undertaking real industry projects with partner companies. Fellows are better prepared for technical roles in industry whilst partner companies benefit from risk-free cutting-edge data innovation and better hiring decisions.

Transition to data science through real world projects. We believe best learning comes from doing. Our curriculum is delivered in-house by the ASI team consists of experienced data scientists and data engineers and business professionals. Workshops cover practical knowledge in delivering data science projects, machine learning, data engineering and soft-skills such as presentation skills, interview techniques and mentorship.

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ASI Data Science & Engineering Fellowship

  • Cost: Free
  • Duration: 8 weeks
Locations: London
Course Description:

ASI Fellowship is a highly selective 8 week programme in data science and data engineering for outstanding PhD graduates and post-doctorates from UK, Europe and US top universities. Since 2014, we have run eight Fellowships with 120+ alumni who now work as data scientists or data engineers. 5 - 10% of STEM PhD graduates in the UK now apply to the Fellowship.

Courses: Data science, Data Engineering, Machine learning, Neural networks, Python, Scala, Spark, Data Visualisation, Business and presentation skills workshops, Interview training

Dates: 3 times a year (starts January, May, September) please check website for the exact start date.

Who can Apply: PhD graduates and Postdocs from a quantitative subject. Each application is looked at individually. Contact if you have any questions. Meet our previous alumni posts on our website.

Deadlines: Please check website for details.

Subjects: Business, Python, Machine Learning, SQL, Algorithms, Hadoop, Data Science

Faculty Reviews

Average Ratings (All Programs)

Job Support


(3 reviews)
  • Anonymous | Data Scientist
  • ASI Data Science & Engineering Fellowship
  • Graduated: 2016

Overall Score


Job Support

"Fantastic experience, got me a great job!"

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

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!

  • Pascal Bugnion | Data engineer
  • ASI Data Science & Engineering Fellowship
  • Graduated: 2014

Overall Score


Job Support

"Got me a job as a data scientist"

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

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.

  • Gary | Data Scientist
  • ASI Data Science & Engineering Fellowship
  • Graduated: 2015

Overall Score


Job Support

"Actually bridges the gap between academia and industry"

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

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.


Faculty's average rating is 5.0 out of 5.0 based on 3 review(s).

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