Introduction to data-analysis with R and reproducible Data Science

Humphrey room 50B


With the increasing availability of data with broad applications (and the sheer size of some of these data), it is more important than ever to be able to elucidate trends, decisions, and stories from data.  Our team will offer a hands on introduction to Data Science and Statistics using the free and publicly available software R. Assuming no background knowledge of software or Statistics, we will bring you up to speed on some of the most useful, modern, and popular data analysis techniques.

This short course is divided into multiple modules.  On day one we will explore the basic features of R and the power of R for constructing visualizations, summaries, hypothesis tests, and statistical models from data.  The modules on day two will cover a gentle introduction to quantile regression and conclude with an in-depth discussion on best practices for reproducible Data Science research and practice using R Markdown and github.


Due to classroom size limits registration is now closed. 


Date Time Instructor


Aug. 31, 2017 9:15am-10:30am Alicia Johnson

Introduction to R

Aug. 31, 2017 10:45am-12:00pm Alicia Johnson Data Visualization
Aug. 31, 2017 1:30pm-2:45pm Nate Helwig

Simple Statistics

Aug. 31, 2017 3:00pm-4:15pm Nate Helwig Linear Regression
Sep. 1, 2017 9:15am-10:30am Lan Wang Quantile regression
Sep. 1, 2017 10:45am-12:00pm Charlie Geyer

R Markdown

Sep. 1, 2017 1:30pm-2:45pm Charlie Geyer Git, GitHub, and RStudio
Sep. 1, 2017 3:00pm-4:15pm Alicia Hofelch Mohr Principles of Reproducible Research