I just finished scraping all the required and optional readings (textbooks mainly) from nearly every Stanford undergraduate and graduate level stats course. https://docs.google.com/spreadsheets/d/1d_MNmIGY7yzrpnStnZqzzYUJc6znWUysfaVTKOvSQSk/edit?usp=sharing I only did the suggested courses for the Data Science track in the MS Stats program. Feel free to do the others and leave links in the comments if you like. This isn't exhaustive (some courses were harder to find than others), so this'll likely be a living document. Feel free to send me anything I've missed or better texts for any subject that I should add to the document. Also, for free technical textbooks, I've found freecomputerbooks.com to be very helpful (despite the fact that it sounds like clickbait). I'd recommend you check that out before shelling out for any of the textbooks listed.

I was trying out the really cool jupyter_pivottablejs library and started to wonder what other smaller, lesser-known, but nevertheless nifty data science tools are out there that I should avail myself of.

What are some lesser known Python data analysis tools that you find useful in your own work? Hopefully we can spread the knowledge around!

I compiled a list of numpy practice exercises related to data analysis. Might be helpful if you want to practice some data munging problems. Feedback welcome!

Link: https://www.machinelearningplus.com/101-numpy-exercises-python/

Anyone else thought that the axis=0 or axis=1 was not intuitive? Well I found out that you can use "index" or "columns" instead. This is much better since it is explicit. Hopefully, they'll mention this more in their docs. Only a few sporadic places mention this.

I'm slowly getting into data science and machine learning with python but I have a very hard time to remember all the methods and stuff. I know, repetition is key , but this is not my job and I can not afford to spend time on data science stuff every day. Therefore i'm looking of for the best cheat sheets around these topics.

Hi /r/pystats,

I got a recommendation to post this here from /r/python, so here you go!

Last night I gave a talk at the Kansas City Data Science Meetup on how to use Data Spyre to create a lightweight and easy to use web application. On top of that, I give examples on how you can incorporate Bokeh and Pygal plots to add interactivity to your web application as well. I've put my Jupyter notebook along with all of my example scripts into a Git Repo for all to use. Enjoy!

https://github.com/pm8k/dataspyre_tutorial