Jake Vanderplas - Statistics for Hackers - PyCon 2016.mp4

We're using this book for a "book club" at work. Doing 1 chapter every 2 weeks. Chapter 1 covers Jupyter, 2 covers numpy, 3 pandas, 4 matplotlib, and 5 machine learning. We just made it through the first 4 chapters and it lays a good foundation for those libraries. I suspect chapter 5 is the meatiest and most interesting chapter, which covers scikit-learn and machine learning techniques. It is a long chapter so we will spend a month on it. I recommend combining this book with McKinney's Pandas book[1] and the author's excellent YouTube presentations at PyCon and PyData. Start with "Statistics for Hackers"[2] by Jake VanderPlas and then look for his others. [1]: [LINK] [2]: [VIDEO]

You might enjoy this talk "statistics for hackers" (in case statistics is not included in your data science knowledge): [VIDEO] For structural beam analysis (ie, from mechanical and civil engineering) check out this textbook on the direct stiffness method: [LINK] Unfortunately, the accompanying code (MASTAN2) is not open source and runs in Matlab. Still, it's a decent first step if you want to move to full-on Finite Element methods, which require much heavier math (real analysis) to really grasp.

>Being able to apply statistics is like having a secret superpower. I totally with this sentence. BUT If you ask for my opinion, merely knowing a list of statistical formulas is not very helpful. Most of the time, people don’t remember the underlying assumptions, so there is a fair chance they will use them in inappropriate situations. I recommend watching these two YouTube videos. The presenters advocate using simulation/bootstrapping/shuffling methods instead of memorizing formulas. Jake Vanderplas - Statistics for Hackers [VIDEO] John Rauser - Statistics Without the Agonizing Pain [LINK]

Feynman's approach seems very reminiscent of Jake Vanderplas's Statistics for Hackers talk[0] as opposed to the purely theoretical physicist approach that the author notes at the end. [0]: [VIDEO]