This course introduces the principles of statistics, data analysis, and R programming. Using datasets from various national and international sports, you will be guided through an interactive data analysis with videos, slides, and reproducible code. There are no pre-requisites for this course, though a familiarity with other programming languages is a plus. Please contact us if there are any questions.

All datasets for this course can be found on out GitHub page.

Part 1: Basics

SectionSlidesCodeVideo
SetupSlides LinkCode Link
Motivating ExampleSlides LinkCode Link
Reading FilesSlides LinkCode Link
BasicsSlides LinkCode Link
Data StructuresSlides LinkCode Link
PackagesSlides LinkCode Link
Probability and StatisticsSlides LinkCode Link

Part 2: Graphics and Manipulation

SectionSlidesCodeVideo
Look At That!Slides LinkCode Link
Polishing PlotsSlides LinkCode Link
LayersSlides LinkCode Link
Dates Times and GroupsSlides LinkCode Link
Plotting Map DataSlides LinkCode Link
Intro to ddplySlides LinkCode Link

Part 3: Machine Learning and Clustering

SectionSlidesCodeVideo
Analytics in SportsSlides LinkCode Link
Machine LearningSlides LinkCode Link
RegressionSlides LinkCode Link
ClassificationSlides LinkCode Link
Introduction to ClusteringSlides LinkCode Link
Hierarchical ClusteringSlides LinkCode Link
Feature SelectionSlides LinkCode Link
Predicting the FutureSlides LinkCode Link
Measuring ErrorSlides LinkCode Link

Part 4: Web Scraping and Development

SectionSlidesCodeVideo
Web ScrapingSlides LinkCode Link
Introduction to ShinySlides LinkCode Link
Shiny StructureSlides LinkCode Link
Shiny ToolsSlides LinkCode Link