OK, peeps, I KNOW y'all have opinions about this, and I want to hear them.
I'm probably going to be out of work for 5-8 months; that's how long it's been after previous layoffs. So I need a learning task.
I want to be able to work with bigger datasets and automate the sort of tasks I now do by hand (data munging, basically). I think I want to learn SQL and Python.
1. Is there something else/extra I should learn?
2. What's the best way to learn these things?
Addendum: I'm on Ubuntu and want to work with F/OSS. I'm willing to learn as much about Linux/Github/etc as needed.
Ideas for projects would also be useful -- e.g. here's a public database and some questions it might be able to answer. I find that's the best way to learn tools, is to have something I need them for.
@sennoma my two cents, as someone who is comfortable in python and dabbled just the slightest bit in SQLite-land:
1. If the goal is to get from dataset to analysis/conclusions as quickly as possible, it’s hard to beat #RStats. The codebase is so tightly integrated for exactly this task that it feels seamless once you get the hang of it. I’m a relative newbie to R (in particular the tidyverse set of packages explained in “R for Data Science”) but I am finding myself reaching for R first when I want to solve a data science type task.
2. OTOH, if data science is a case study for learning skills that would be transferable for more traditional software-engineering, then python + SQL is definitely the way to go. Or if you want to go all in on machine learning—a lot more widely used tools for ML in python.
@sennoma another benefit of #RStats is the super friendly and engaged #RStats community, especially on Mastodon! There’s a big focus on tutorials/blog posts, and I’ve been super impressed how much R I’ve been able to pick up just scrolling my feed here.