I was looking at a statistics course (undergraduate degree), and I thought I probably would find doing only statistics a bit dry, and I thought about if doing an Applied Mathematics major (I am better with applied math than purely statistics, however, I'm still good with stats.), with perhaps a couple more electives in statistics, as opposed to a statistics major. E.g. on top of the applied math course (Real/Complex analysis, ODE's, PDE's, vector analysis, fluid flow, calculus), also do some stochastics courses (statistical analysis, stochastic analysis, SDE's). This way it's a bit more diversified because I feel like I'd do better if I have a bit of both.

How relevant would some of the applied math topics be to data science? I may also do some data science electives as well.

This is of course on top of knowledge in python, R and SQL.

  • $\begingroup$ What do you want to do in data science? $\endgroup$ – Dave Jul 6 at 13:59

I'm a student of Applied Mathematics, who is working in the field of Machine Learning. Yes, studying Applied Mathematics will definitely help you, when you pursue Data Science.

However, when you're deciding whether you should pursue a field as a degree, you need to have a holistic view of the situation.

Now, when you are deciding to pursue Applied Mathematics with the specific purpose of working the field of Data Science, one of the factors, is the desire to apply what you learn in your course to your work. However, as the Mathematics curriculums around the world are designed, the rigour is concentrated at understanding and breaking down a problem into its basics and using you logical thinking to prove or disprove statements. That is what most of Mathematics revolves around. However, when you would come to apply these, this understanding although would aid you, you would ideally be applying to say roughly 10% of what you've learnt during your course.

Now to spend 3-5 years (on the basis of the course), to study something deeply and intricately to ending up using just 10% of that would be a bad trade-off. It'll help you in understanding the algorithms like gradient descent, the mathematics behind most machine learning algorithms, and papers. However, in daily use, you'd not be using much of it.

If you do wish to study a quantitative degree, a degree in Statistics would help you way more in Data Science, rather than one in Mathematics. However, a far more prudent decision would be to study engineering in a computer science-oriented field and choose electives that are Mathematics and Statistics heavy throughout your course.

Right now, the top colleges and universities around the world offer courses in Data Science, and if you have already decided to work in this beautiful field, a course that caters to students who are interested to contribute to this field, is by far the most prudent choice. It'll give you the optimum exposure to the required Mathematics and Statistics, along with the technical tools to apply them to achieve real-world tasks, something that drives every domain.

Placements ain't something that should drive your decision, however a degree exclusively in Data Science would also help you in that direction.

Having said all this, Mathematics is the driving force behind everything in Data Science. And a rich understanding of Mathematics would help definitely propel you towards more success in this field.

Best of Luck!

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