I'm an Imperial College graduate, so maybe I'm biased here, but my alma mater is emerging as one of the top schools in the world for data science talent in finance.
This isn't a huge surprise, after all Imperial College is well known for specialising in STEM subjects, and it also has its own business school and new Data Science Institute. However, Imperial graduates' preeminence in data science pre-dates the institute, which was founded in 2014. Many of the subjects and inter-disciplinary degrees traditionally taught at Imperial lent themselves to data science and were in existence well before the term became fashionable. There is for example the joint Mathematics and Computer Science degree at (which I studied). There’s also the Electronic and Information Engineering degree, which is taught in the Computer Science and Electrical & Electronic Engineering departments. Perhaps more uniquely, there’s also the Physics and Music performance degree which is taught jointly with the Royal College of Music.
Data science is after all an interdisciplinary field. Studying joint degrees where you must balance different disciplines is likely to be useful. Being a good data scientist in finance isn’t simply a matter of knowing Python or R, although coding is clearly a prerequisite. It’s also important to have a good grasp of statistics, as well as having an understanding of the finance domain. Understanding the domain properly can help you to focus on the problem you want to solve.
To get a data science problem implemented in production also requires engineering skills and the ability to make code robust and scalable. It’s likely that you’ll be working in a team with people with different skillsets. You’ll also be facing clients who are probably more interested in finding a solution, rather hearing about the difference between a linear regression and random forests. Hence, soft skills are also important. I found the group projects I did at Imperial College were a good way to help develop these soft skills and to learn how to (or indeed not to) work in a team.
Obviously, Imperial isn’t unique in teaching these types of interdisciplinary courses. - They are popular in the USA., where Columbia's Masters of Financial Engineering rules the roost. The undergraduate course at MIT on computer science, economics and data science is also interesting. Mark Zuckerberg famously studied in Harvard attending modules in both computer science and psychology, before dropping out to focus on Facebook.
I’m certainly not going to say that the best data scientists only studied at Imperial. What I can say though is that, at least personally speaking, Imperial College did provide me with the skillset that I use on a daily basis to solve data science problems that I’ve coupled with domain knowledge of financial markets which I’ve built up after graduation. I learned both practical skills like coding and statistics, as well as the more general skill of how to approach difficult problems at University and I know plenty of other Imperial graduates who would say the same.
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