So you want to crack finance and you want to make a lot of money. Nothing wrong with that, but how can you best get an advantage in this really competitive game?
The first step is to choose the right school and the right major; if your university is a so-called “target” school (i.e., a school that investment banks and consulting firms actively recruit from), your chances of getting into the game on the most direct career path are automatically leveled up. And while that used to be enough in the past, these days another big factor is your major.
Obviously, the most appropriate major for finance would seem to be…finance. But things aren’t always what they seem, and finance is not the only–or even the most desired–major for investment banks and the like.
Economics has historically been a preferred major, in part because the math is a touch more complicated than finance and because of its broader, more theoretical approach to financial and economic problems. But a new major is quickly taking over, even ahead of econ and finance: data science.
Just a generation ago, this major did not exist, but it has quickly grown out of computer science as its own sort of specialty, and the sheer volume of data in the world today has compelled CS departments to build out a specific field for analyzing this data. With large parts statistics, the best data science programs are very math focused. And that’s why they appeal to investment banks, who want analysts that can structure mathematically complicated products with ease.
Another big part of data science is programming. Just about every data science major will involve an in-depth education in Python, which has quickly become the gold standard for analyzing data. This software helps users make sophisticated analyses of large datasets, and it has become essential for analysts today (somewhat ironically, as many senior bankers have little to no knowledge of Python at all).
These two skills will combine to give a starting analyst a head start, but that doesn’t mean you should switch majors today. Many roles in finance involve other skills–sales roles require a sharp critical mind and a sensitivity to human emotions, psychological biases, and complex social dynamics, for instance. Analyzing a brand new industry involves much more than in-depth knowledge of data, because data is by definition backwards looking. For some roles, having a different background can be incredibly valuable–although those roles will involve a much less conventional trajectory than graduation > investment banking > higher up in high finance.
Then there’s the fact that some data science programs aren’t, well, really all about data science. The popularity of this major has meant some universities have developed the program in name only. Likewise, the importance of data science means that it’s been integrated into finance programs at other schools, meaning a finance major there may have more Python skills than a data science major elsewhere.
In short, data science has taken a lead on finance in recent years, and the preference for data science might very well continue for some time to come. But you need to really dig into what a university’s program truly offers before deciding which will be better for you–and which will give you a better chance of securing a job (or, for that matter, being able to handle the job once you’ve gotten it).