After seeing the big thread about data-science, I got kinda interested to learn some of it for fun. I have a CS background, distributed systems, backend and embedded. Though I work with what I call pseudo NLP, which includes text parsing and cleaning, simple labeling, as well as pseudo AI, such as fuzzy string similarity, TF/IDF, fuzzy search, random forests and simple decision tree models, some simple regressions, use of forward chaining rules a la expert systems, some logic backward chaining rules as well here and there, etc.
When I learn, I like to start with real problems and exercises and work backwards, a bit in the style of the Feynman Technique.
But, I havn’t been able to find exercises and realistic problem exercises (think real business problems you’d get on the job as a data scientist) for me to go through and learn data science.
Anyone can list out or point to resources that have proper exercises and realistic problem statements which I can go through to learn me some data-science ?
P.S.: I’ve tried Kaggle, and while it has data sets to mess with, it doesn’t seem to have exercises. Like it’s very much just, here’s data you can mess around with.