Materials

Class 22: Course wrap-up

June 20, 2018

Slides

Class 21: Modeling III

June 19, 2018

Slides

Model selection activity

June 19, 2018

A guided activity to practice building univariate and multivariate linear regression models and how to perform model selection using k-fold cross-validation.

Class 20: Modeling II

June 18, 2018

Slides

Class 19: Modeling I

June 15, 2018

Slides

Class 18: Inference and simulations IV

June 14, 2018

Slides

Class 17: Inference and simulations III

June 13, 2018

Slides

Class 16: Inference and simulations II

June 12, 2018

Slides

Class 15: Inference and simulations I

June 11, 2018

Slides

Class 14: Midterm project conferences and R questions

June 08, 2018

Slides

Class 13: Web scraping II

June 07, 2018

Slides

Web scraping II activity

June 07, 2018

Continuation of web-scraping activity.

Class 12: Web scraping I

June 06, 2018

Slides

Web scraping activity

June 06, 2018

Interactive demo on how to use the rvest web-scraping tools.

Class 11: Introduction to the Midterm Project dataset

June 05, 2018

Slides

Class 10: Tidy data

June 04, 2018

Slides

Tidy gradebook activity

June 04, 2018

Interactive demonstration showing how to apply the Tidy Data principles to a typical classroom gradebook.

Class 9: Statistical distributions II

June 01, 2018

Slides

Chicago vehicle towing data activity

May 31, 2018

Additional practice with the dplyr using vehicle towing data posted by the city of Chicago.

Class 8: Statistical distributions I

May 31, 2018

Slides

Class 7: Data Wrangling II

May 30, 2018

Slides

dplyr demos II

May 30, 2018

Continuation of the interactive demonstration of the major features found in the dplyr package.

Cumulative distribution functions

May 30, 2018

Reading about how to use R to compute, visualize, and apply percentiles of a dataset.

Probability mass functions

May 30, 2018

Reading about how to connect probabilities with values in a dataset.

Class 6: Data Wrangling I

May 29, 2018

Slides

dplyr demos I

May 29, 2018

An interactive demonstration of the major features found in the dplyr package.

Describing univariate and bivariate data

How to write about visualizations of univariate (one variable) and bivariate (two variables) data.

Class 5: Introduction to data and visualization II

May 25, 2018

Slides

Class 4: Introduction to data and visualization I

May 24, 2018

Slides

RMarkdown practice activity

May 23, 2018

Practice editing RMarkdown files and saving to Github.

Class 2: The data scientist’s toolbox

May 22, 2018

Slides