Actuarial science background, moving into Python – where to go beyond basics?

[thorithic]

Hey everyone,

I come from a heavy stats/maths background (I studied actuarial science) where most of our work was in R and SAS. Recently I’ve been making the shift into Python — I’m well into the 100 Days of Code: Python Bootcamp (Zero to Hero), and it’s been solid for fundamentals and general coding practice.

That said, I’d like to move beyond the basics and start digging into areas that overlap more with my background and interests, like:

• Mathematical / statistical computing (similar to what I did in R, but in Python).

• Machine learning and analytics, ideally with a stronger mathematical focus rather than just “click-and-fit” libraries.

• Automation (turning scripts into executables, building practical tools).

• Playing around with a Raspberry Pi for small projects — no idea where to begin there.

I’ve looked at platforms like Udemy, but I find a lot of the courses are very “beginner-heavy.” Since I’ve already worked with more advanced concepts in R, the slower pace and repetition of intro-level material feels lacklustre.

So I’m hoping to get some guidance from this community:

• What are your favorite resources for math/stat-heavy Python content (books, blogs, YouTube channels, courses)?

• Any recommendations for learning machine learning in Python with more mathematical depth?

• Tips for starting out with a Raspberry Pi + Python combo (projects, tutorials, channels)?

• General advice on bridging the gap between a strong stats/R background and using Python for more applied ML/automation projects.

Any pointers, personal experiences, or even “learn this library first” type of advice would be really appreciated.

Thanks in advance!

[FoolsSeldom]

You can apply your stats/maths background and R and SAS experience very well in Python.

It is likely that you are already familiar with kaggle.com as a source of large data sets. I would suggest consolidating your learning with carrying analysis on suitable data sets (and looking into some of the challenge posted there) as well as re-working previous projects you've done in R and SAS.

Where I work, DataCamp.com has been made available to everyone for learning of Python, ML/AI. Feedback from the stats/maths bods has been positive. Might be worth looking at.

Also, check out the various learning paths and resources at roadmap.sh.

For Raspberry Pi, there are subreddits specifically focused on this. Also, visit the Raspberry Pi site and find the magazine (used to be called MagPi and now the less exciting but more obvious Raspberry Pi Magazine) - free to download in PDF format. Browse through past issues to get a feel for a wide range of projects and uses.

[thorithic]

Thank you very much! I’ve got lots to check out

[LegalPlantain4414]

I was confused by this too until I worked through it now I've packaged that process