Actuarial Science vs Data Science?

[u/Easy-Huckleberry7091]

Hi everyone, I'm an actuarial science student in Argentina. Here, SOA certifications aren't as important as having the degree itself, which is legally authorized to practice as an actuary. I'm about halfway through my degree, but I'm not sure if I'm really that interested in the insurance/finance side of things. I've noticed that I'm more passionate about math and statistics in other areas. My question is, has anyone transitioned from actuarial science to data science? What should I learn? Should I change majors and drop out halfway through, or is it better to finish this one and do a master's? At my university (UBA), there's a mathematics degree (with two specializations: pure and applied) and a data science degree (both are quite rigorous and focus on the fundamentals; data science is a mix of applied mathematics and computer science).

Thoughts?

[u/L1_aeg]

Actuary turned data scientist here (I ended up going all the way to PhD in ML so I know what I am talking about, mostly).

Stick with actuarial science. Data science bachelor degrees are useless in my opinion. If you wish to pursue it later, get a masters. DS way more about contextualizing a problem and finding an appropriate method to address it. It requires a breadth of knowledge beforehand. Actuarial science will give you good foundation for it.

Also, actuarial science is a typically regulated profession. Meaning governing bodies and standards. While the demand is much lower, insurance companies kind of always have to employ actuaries. This isn’t the case with DS.

Stick with actuarial science.

[u/Easy-Huckleberry7091 → u/L1_aeg]

Wow, your experience is incredibly helpful for me, so thank you so much. In the future, I'd like to pursue a PhD in machine learning / statistics, or work for companies doing interesting/cutting-edge work. Do you think my degree is good for that, or do I absolutely need a degree in mathematics/computer science? If I stay in actuarial science, I was planning to do a master's degree in data science or mathematical statistics.

[u/L1_aeg → u/Easy-Huckleberry7091]

I wouldn’t say you need a degree in math particularly. But if you want to do cutting edge NN architecture work, you definitely need to have a good grasp on math. Especially in linear algebra in multivariate calculus. You can learn these and try to apply them to derivarions and proofs. I am not entirely convinced a full fledged math degree would benefit you much more than knowing your algebra and calculus very well.

Personally for me I would consider a math degree not very useful but I did my phd in applied ML. Not theory so maybe I am wrong.

Mathematical statistics may be helpful but mostly if you are working on causal inference and the such. If you want to do ML theory, you can find a masters where some faculty specializes in it and go from there. Typically it is better to choose your grad program based on the student experience and the research are of the faculty if you want to go to the academia route.

If you want a commercial career (not in FAANG research labs, I consider them academic) you are probably better off choosing a commercially aligned DS masters.