Jake Vanderplas - Statistics for Hackers - PyCon 2016.mp4
Jake Vanderplas - Statistics for Hackers - PyCon 2016.mp4 on HackerTube: recommended by 3 Hacker News commenters across 3 separate months, 2017–2025.
Jake Vanderplas's PyCon 2016 talk shows how sampling-based methods such as simulation, shuffling and bootstrapping make statistics intuitive. His pitch is that if you can write a for-loop, you can do statistics.
What HN commenters said
4 Hacker News comments link this video. Each one links back to its thread.
armanboyaciMar 2025Recommends
>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 ▶ this video John Rauser - Statistics Without the Agonizing Pain [link]
in “Statistical Formulas for Programmers (2013)” · View on HN
auxymJun 2020Recommends
You might enjoy this talk "statistics for hackers" (in case statistics is not included in your data science knowledge): ▶ this 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.
in “Ask HN: Recommendations for applied engineering and math books for programmers?” · View on HN
pixelmonkeyAug 2017Recommends
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]: ▶ this video
in “Python Data Science Handbook” · View on HN
faitswulffAug 2017Cites it
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]: ▶ this video
in “Feynman on Fermat's Last Theorem (2016)” · View on HN