Should financial professionals learn data science?
When many financial professionals are asked about the importance of learning programming languages, they usually respond with answers that imply such tools are only beneficial for IT or Development teams. They believe that learning such languages will provide little to no benefits to them because, after all, they have teams who can do that for them.
Here’s the flaw with such a perspective: financial professionals lose the time advantage when they rely on IT teams to complete tech-related tasks given the growing backlog of requests! And, tech teams veer away from what they are meant to be doing which is developing IP that can help their organisations develop and sustain competitive advantages. Obtaining programming skills means that professionals can more easily work with complex data. And, as we know, data is the new oil – the ability to capture insights from data to drive decision-making is critical to being competitive in the future. If financial professionals shun away from learning technical skills, they will soon become obsolete in our increasingly technological world.
What language should you learn first?
Python is a third generation programming language that improves some of the syntax annoyances of prior-developed programming languages. The language uses a combination of white space and indentation to relieve programmers of typical syntax mistakes. As a result, it tends to be an easier language for new programmers to learn in a shorter amount of time.
When it comes to data-related tasks, Python is one (if not the) most popular programming language. It has incredible global support and so many questions are already answered through technical forums, which makes Python users extremely efficient in debugging their own code. And, it is used as one of the primary data science and machine learning tools and a go-to language for . In brief, Python can be seen as a 360-degree solution that helps finance professionals better interface with a variety of technical teams.
Furthermore, Python can help financial professionals automate tasks and boost their efficiency at work. Can you recall those tasks that take hours of mindless repetitive work to complete? Well, if you learn Python, there is a great chance you can build scripts that automate such work for you freeing you up to focus your energy on more interesting and impactful work.
Lastly, when it comes to data analytics, one ongoing debate is about whether Microsoft Excel is better than Python or vice versa. The short answer is both are great and have their purposes.
It really depends on the underlying task that needs to be completed in determining whether Excel or Python is the right tool. If the task is building a three-statement financial model and valuations, then Excel is probably the better option because the analytical horsepower needed for such tasks is minimal. If a task is about finding clusters in millions or billions of data points with thousands of independent variables then Python and its powerful data science libraries are most likely a better choice because Excel was simply not designed for such data-heavy tasks. Data analytics power users integrate both Excel and Python in their day-to-day work as both tools together are synergistic and incredibly powerful.
Fancy picking up some Python programming skills? To help you master new technical skills and learn the latest techniques and industry best practices in data science, CFA Society United Kingdom, in partnership with Cognitir, is hosting a series of data science bootcamps this autumn. Visit the CFA Society United Kingdom website for more information.
About the Author:
Neal Kumar is a co-founder of the global training company, Cognitir. Cognitir helps business and finance individuals develop in-demand tech skills so that they can be more efficient and data-driven in their roles at work.