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As a data science software consultant for the Stanford libraries, I often hear people complain that R is slow. I wrote this blog post to demonstrate that R code can oftentimes be re-written to be much, much faster.


I wrote this as part of taking Hadley Wickham’s Readings in Applied Data Science course at Stanford in the spring of 2018.

Introduction

As data science skills become increasingly important, the teaching of data science becomes increasingly valuable. In the context of data science, I think of teaching not as the traditional process of talking at a whiteboard or grading worksheets, but instead as the process of creating environments and designing experiences that lead to measurable learning for students.

As a matter of definition, I am interested in developing in students the ability to extract value out of data (which I’ll refer to as data science). The students I am interested in are anyone with the numeracy background to not need additional training in, say, algebra, but not necessarily with backgrounds in either programming or any level of serious statistics. …


I noticed in basketball why I’m bad. …

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