We offer two entry routes into the Fellowship. One route is aimed at PhDs and post-docs, and the other at experienced software engineers who want to move into data science. However, we review each case individually so if you don’t quite fit into one of those groups, get in touch and discuss it with us.
We don’t have strict requirements based on years of work experience, but you do need to be comfortable with writing commercial quality code and keen to learn Python and Spark. Maths and stats experience would be a plus but not essential. We review each case individually. Get in touch and discuss it with us.
The Fellowship is intended to be a stepping stone to a new career, so we ask that applicants be available to start a job in London after the fellowship. If you want to pursue your PhD - that's great - look us up when you're thinking about finishing.
The fellowship is intended to be a stepping stone to a new career in, so we ask that applicants be available to start a job in London after the fellowship. If you're interested in finding out more about data science and engineering, we suggest you come to our Data Science Labs Meetups.
We review applications approximately 6 weeks before the start of the programme. Please expect to hear from us around this time.
We send out offers to approximately 10% of candidates.
ASI is a world leader in data science consulting, education and data innovation. As businesses realise the importance of using data to drive growth and stay competitive, ASI is at the forefront of using data to make better decisions. Be it running the UK’s most prestigious data science fellowship, or helping some of the most well known companies in the UK solve their data science problems, the scope of our mission is beyond that of a traditional startup.
We are a startup on a mission. As physicists, computer scientists and economists, we saw many very talented people struggle to find jobs after their PhD’s, only to excel once they got a foot in the door. The ASI Fellowship exists because it is an opportunity that we wished were available when we were finishing our PhD’s. This is a simple market failure – companies cannot afford to take a chance on candidates that come from backgrounds they are not well equipped to assess. The ASI Fellowship takes candidates and provides them with the knowledge, opportunity and mentorship necessary to create a body of work that is business-relevant. This adds value to the candidates, and creates a no-lose proposition for our partner businesses. We believe this generates real economic value and allows everyone to succeed.
Three. Spring (starting January), Summer (starting May), and Autumn (starting September).
The Data Science and Engineering Fellowships are Autumn 2016, followed by Spring 2017.
Project are assigned based on mutual preferences expressed by both the Fellows and the companies participating in the Fellowship. A networking event is organised at the beginning of each Fellowship with the aim of finding a good match for each Fellow.
The Fellowship is based in central London. Apart from taking classes and working data science, we organise many different interesting talks, activities and social events during the Fellowship. Staying in London allows our Fellows to make the most of the experience.
Data science and data engineering encompass many fields, knowledge and techniques. The modular structure allows the fellows to follow a customised programme aimed at enhancing and complementing their current skillset.
The curriculum covers machine learning, data engineering, SQL, NoSQL, Spark, data visualisation and a range of business skills. The main component is a project with a partner company solving a real business problem using data science and/or data engineering. Fellows can then use this project to showcase their skills.
Fellows receive 2 weeks of intensive training at the ASI. The curriculum covers machine learning, databases, distributed computing, data visualisation and a range of business skills. The main component is a project with a partner company solving a real business problem using data science and/or data engineering, during which you are assigned an ASI mentor.
This is the time to present your project you’ve worked on during the fellowship to hundreds of potential hiring companies. Make the most out of it! You can see videos from previous demo day presentations on the Fellowship page.
Python is the most widespread language in data science and it is important you become comfortable working in Python before the course. Learning to store and query large databases in a standard format is also fundamental to becoming a data scientist, so understand a version of SQL and some database theory will also help you. A good understanding of statistics is equally important to help you understand the concepts we cover in Machine Learning.
Apart from doing data science, we are organising different interesting talks, activities and social events. Staying in London allows our fellows to make the most of the experience. Let us know if you have difficulties sorting it out.
How much does the fellowship cost?
We are not directly involved in finding accommodation for our Fellows. We are more than happy to recommend websites to help you in your search.
No! We are very careful to put the fellows best interests first and if the company suits you, then great. Otherwise we'll support you wherever you want to go.
Fellows can expect to be interviewed by many companies towards the end of the fellowship. We will help our fellows to be the best candidates in the market.
We will have to look at individual cases. Get in touch with us.
We do not currently work with companies internationally - 95% of our partners are based in the UK. Nonetheless, we work with big firms who have offices around the globe.
Unfortunately, we do not finalise the list of participating companies until shortly before the Fellowship starts. The list of companies varies from cycle to cycle. We have worked with companies in almost every sector, from energy, to transport, to media. We recommend browsing past projects and presentations to get an idea of companies we’ve worked with.