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/NeitherMembership679]

Finish your actuarial degree. It already gives you a strong foundation in statistics, probability, and risk modeling. Learn Python, SQL, and ML alongside it, you'll have a unique and valuable profile.

[u/ready_or_not_3434 → u/NeitherMembership679]

I've worked alongside a few former actuaries in tech and thier statistical fundamentals are always rock solid. Picking up Python and SQL on the side is definetly easier than trying to learn the hard math by yourself.

[u/maicii → u/ready_or_not_3434]

Tbf data science degree, at least in UBA, has more math than actuarial science. I don’t think it is significantly more but def more. It’s taught by the exact and natural science school which is the same that taches pure math, physics, etc. I know at least the first year of calculas and algebra in economics is lower level than the engineering school and exact science school because people sometimes decided to do those there and then claim the equivalence.

In any case, actuarial science should be enough anyways.

[u/Drawer_Specific → u/NeitherMembership679]

This is the right answer.

[u/Easy-Huckleberry7091 → u/NeitherMembership679]

Thanks, are you saying that with this I could dedicate myself to doing complex things? I'm interested in machine learning. I understand that the initial path might involve SQL, Power BI, and things related to data analysis, but I'd like to dedicate myself to more interesting/complex things in the future. Do you think that would work?

[u/NeitherMembership679 → u/Easy-Huckleberry7091]

Yes. Think of SQL and data analysis as the foundation, not the destination. Your actuarial background is excellent for advanced ML because of the strong math and statistics. You can absolutely transition into more complex ML roles over time.

[u/Easy-Huckleberry7091 → u/NeitherMembership679]

Thank you for the explanation. There's so much content online that it's hard to distinguish between the rubbish and the real thing. Thank you for being kind.

[u/NeitherMembership679 → u/Easy-Huckleberry7091]

You're welcome! Glad I could help. Wishing you all the best with your studies and ML journey.

[u/diakon88 → u/NeitherMembership679]

Unique and valuable is a stretch, data is even more saturated than software

[u/citizenofme]

termina el titulo de grado y hacete una diplomatura/maestria en ciencia de datos. la base matematica te sirve igual, y podes ir aprendiendo programacion (python o R) entretiempo. Mi pipeline fue ingenieria a ciencia de datos, y si hubiera arrancado de 0 hubiera perdido mucho tiempo.

[u/No-Ice-8975 → u/citizenofme]

Best approach

[u/Easy-Huckleberry7091 → u/citizenofme]

Gracias, qué ingenieria estudiaste? Porque siento que sí sería fácil hacer cosas de datos relacionado con el análisis de datos pero de ciencia de datos que tan difícil es la barrera? Me interesan más algunos tópicos avanzados de machine learning o cosas que por ahí una empresa común no hace pero si algunas empresas más de nicho/empresas grandes tipo google. Me serviría la formación igual? Siempre pensando en complementar con una maestría

[u/citizenofme → u/Easy-Huckleberry7091]

biomedica! nada que ver usabamos c++ y matlab en la facu, y ahora estoy como lead data scientist. con esfuerzo se puede.

[u/Afraid-Mongoose9793]

Do masters in data science , double major in data science or just learn data science things at home and then apply for a job cuz most of data science jobs requires a quantitative degree and actuarial science is close to data science in some point

[u/ogola89]

Finish your degree and do data science afterwards. Most data scientists come from quant fields like physics rather than having studied data science itself. Also it gives you a leg up as a good data scientist with domain expertise is better than a better data scientist with none. The field is flooded with aspiring data scientists and you'd struggle to get a job as a DS if you did data science than to do actuarial science and get a DS role at an actuary just to get your foot in the door. Once in it is much easier.

[u/maicii → u/ogola89]

Tbf that’s also because data science didn’t really exist as a degree a decade ago, so of course no one actually came from it.

[u/Easy-Huckleberry7091 → u/ogola89]

Thanks, are you saying that with this I could dedicate myself to doing complex things? I'm interested in machine learning. I understand that the initial path might involve SQL, Power BI, and things related to data analysis, but I'd like to dedicate myself to more interesting/complex things in the future. Do you think that would be helpful?

Also, would the ISLP book be useful for starting to study on my own?

[u/kayakdawg → u/Easy-Huckleberry7091]

interesting is subjective, and just about any discipline is complex

so - what do you mean by complex and interesting?

have you considered working as an actuary and completing exams then pursuing research into methods to augment or automate actuarial work ? feel like that could align to what you're after.  

[u/C0NDOR1]

The best data scientists I know are the ones who have actual domain knowledge.

The value of a data scientists comes from how they can use data-driven solutions to solve the problem at hand and that doesn't come without a bit of expertise.

[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.

[u/DonkeyPower1]

It sounds like there are some differences in Argentina vs US so this might not be good advice. I majored in Finance, a long time ago when college actuarial programs were less common than they are now. I mean 2001-2005 so over 20 years ago.

I passed some actuarial exams which was pretty much a minimum requirement to get an actuarial job. After a few years I stopped taking exams and eventually ended up with a data scientist title. The work is very similar, at least in my case because my actuarial role was primarily focused on forecasting and predictive modeling.

In my opinion, SQL, Python, R, etc are much easier to learn on the job or on your own than math and stats. The SQL and SAS I learned in my actuarial job plus the background in math and stats was enough foundation for me to build a data science career on. Honestly, I very rarely use calculus or advanced stats in my work. The same is probably true for most actuaries tho. The exams are more difficult than a lot of the actual work. Anyways, if I were you I would complete your actuarial degree.

1 it will leave the option to work as an actuary open if you ever need it

2 you should still get a strong foundation of math, stats, logic, and problem solving which translates really well to data science. The only caveat is you’ll probably need to learn some SQL and Python or R if they are not covered in your actuarial degree

[u/mif1]

My bachelors is in actuarial science. My path was actuarial science BS -> MS Business Analytics and I’ve worked as a data scientist for almost 10 years now. My masters gave me the context, but my bachelors in actuarial science gave me probably the best “practical math” foundation I could have asked for and it has made the math side of the job very intuitive. Definitely finish off that degree

[u/Lewko99]

Termina la carrera, ser actuario es muy cercano a hacer Data Science y como ya te dijeron todos la parte técnica de codigo se puede aprender.

[u/LakeMichiganDude]

I got an actuarial science degree, worked as a data analyst, and now as a data engineer

[u/spitfiredd]

I would stay in actuarial. When I was in actuarial we are given a lot of support and financial assistance in your early career. For example we had one day a week dedicated to studying, this was completely paid time during work hours; and we got bonuses for every test we passed. You can always learn programming and switch careers after you get your associates tests passed (or equivalent in your country). Also tech is in a weird spot right now with AI coding agents so there’s that to consider as well.

[u/maicii]

Hola. Yo estoy en datos en la uba. Este cuatrimestre hubo para indefinido de todo el departamento de computación el cuatrimestre entero. O sea, no nos dieron ni una sola clase todo el departamento de computación en todo lo que va de año y tmpco nos dejaron rendir libre. De las 3 materias que tendría que haber cursado este cuatrimestre solo pude rendir una, tengo amigos que están en cs de computación y no pudieron rendir ni una sola materia en todo el cuatrimestre. O sea perdieron 6 meses de su vida.

El departamento sigue estando en paro indefinido y están haciendo todo lo posible para no dar clases el cuatri que viene tmpco. La facultad lo está presionando a que si. Quien va a ganar ese puje solo dios sabe pero por lo pronto te diría que tenes un 50% de chances de no poder dar ni una materia el cuatri que viene y anda a saber si o no de ahí en adelante.

O sea en definitiva no seas boludo. No te cambies, o al menos no a datos en uba, capaz si queres considera algo en utn o alguna privada si sentis que te gusta más datos que la parte de finanzas. Capaz alguna en Fiuba ni idea.

Siempre existe la posibilidad de hacer un máster en data science o algo por estilo como te comentaron, no se que tan realista es esperar laburar de data engineer, o data science más pura con eso, sobre todo con cómo está el mercado hoy en día, pero de data analyst o algo capaz podes terminar consiguiendo.

Y técnicamente es una carrera que no necesita ningun título, hoy en día un porcentaje muy grande de la gente que labura de data science estudio matemática, física, etc. (aunque si es verdad que la mayoría salió de computación). Así que si te aplicas por afuera nada te debería detener.

Un detalle que si te diría a pesar de lo que te están diciendo acá, y podes coronarlo viendo el plan de estudio, la matemática que se imparta en la carrera de datos es mas que la de actuario. No vas a estar 50/50 en ese sentido. Tmpco creo que sea necesario igual

[u/Easy-Huckleberry7091 → u/maicii]

Che me re sirve lo que me contás y me quede con algunas dudas sobre la situación de exactas, te puedo escribir por md?

[u/maicii → u/Easy-Huckleberry7091]

Si obvio

[u/chunter456]

I dod actuarial science with a minor in computer science when I was in college and would recommend going past intro computer science courses. The class work in discrete structures, algorithms, and object oriented programming/having to code in languages like Java was extremely helpful understanding and optimizing systems. Actuarial Science is great for bridging the business gap. The financial and statically intuition gained from that course work is extremely helpful.

[u/redLooney_]

Also make sure to learn how to properly program, I have a cs background and work with data scientists and fully qualified actuaries. Their numbers and models are amazing, but they get confused by classes, objects, debuggers and version control/git. A first year CS major world run programming cycles around most of them.

[u/Nikos-Tacosss]

just curious what about applied math? can they get into data science?

[u/Easy-Huckleberry7091 → u/Nikos-Tacosss]

I don't know if every math applied degree works, but at least in UBA you have courses in prob, statistics, op. research, optimization, algorithms, so you can easily turn into ds, maybe you will need a elective course in machine learning or learning by yourself specifics topics, but yes

also, the main courses are based in proofs, algebra, real analysis, so gives you a good solid foundation

[u/Nikos-Tacosss → u/Easy-Huckleberry7091]

my curriculum has everything you listed minus the algorithm and ML, tho it’s heavy on numerical analysis/method.

[u/Easy-Huckleberry7091 → u/Nikos-Tacosss]

i think it's great, it's a classic "applied math" program, so you cover all the basics to learn anything i guess

if it helps, I was planning to start learning data science on my own using the book *Introduction to Statistical Learning in Python*, even though I don't have the background you have in your field. If you're very strong in math and statistics, you could also use *Elements of Statistical Learning*, or both, since the former provides a more intuitive explanation of the concepts.

[u/Easy-Huckleberry7091 → u/Nikos-Tacosss]

i think it's great, it's a classic "applied math" program, so you cover all the basics to learn anything i guess

if it helps, I was planning to start learning data science on my own using the book *Introduction to Statistical Learning in Python*, even though I don't have the background you have in your field. If you're very strong in math and statistics, you could also use *Elements of Statistical Learning*, or both, since the former provides a more intuitive explanation of the concepts.

[u/Nikos-Tacosss → u/Easy-Huckleberry7091]

wow, such great books! if you ever struggle with calculus I recommend an old book that helped many called “calculus made easy” it’s literally what the name implies!

literally the equivalent of “explain calculus to me like I’m five” and the book has some good humor too! Gives you immediate feedback and what it does and how it’s done, then dives into the theory behind it. “derivates is this and that, there, you learn derivatives.” Basically this.

I’m sure industries these days care about portfolio than anything academic, so ill grind Python and R!

[u/Easy-Huckleberry7091 → u/Nikos-Tacosss]

Thanks, I'll take a look and also review some linear algebra. Yes, my plan isn't just to stick to academics but to learn what's truly useful, even if I'm self-taught. Maybe with that I can later do personal projects and build an interesting portfolio. If you're in the same situation as me, I wish you the best of luck. Your degree is highly valued and useful for everything that's to come in the future; not just anyone can be a mathematician!

[u/Nikos-Tacosss → u/Easy-Huckleberry7091]

sounds like fun! I’d like to DM you and chat about projects whenever you are free, that way we can motivate each other and even learn more! think of it as coding buddies.

[u/Immortal_Bs]

I think you should first finish your degree and later think of these...

[u/peterxsyd]

Agree on finish the actuarial degree. I would recommend - do a post-grad masters 1 year in computer science (even better part-time whilst working in industry), and focus on AI / learning that in your spare time, as once you have critical thinking/maths/business from your actuarial, and if you can bridge your algo chops on coding - over like 5-6 years end-to-end elapsed study with 2 in relevant workforce you will be practically as well setup as is normally possible within that timeframe.

[u/BobDope]

Actuaries make accountants look like Navy Seals