#Data Science

Introduction I’m going to use this post to discuss some of the aspects of data science that interest me most (tidy data as well as using data to guide strategy). I’ll be discussing these topics through the lens of a data analysis of results from a few high school golf tournaments. I’m going to take a little bit of time to talk about tidy data. When I scraped the data used for this analysis, it wasn’t really stored in a tidy format, and there’s a good reason for that. [Read More]

This post will provide an R code-heavy, math-light introduction to selecting the \(k\) in k means. It presents the main idea of kmeans, demonstrates how to fit a kmeans in R, provides some components of the kmeans fit, and displays some methods for selecting k. In addition, the post provides some helpful functions which may make fitting kmeans a bit easier. kmeans clustering is an example of unsupervised learning, where we do not have an output we’re explicitly trying to predict. [Read More]