This page contains course material such as class slides, practice problems, and tutorial assignments.

Week 0

January 5 Tutorial

There are no tutorials on January 5. Instead of attending tutorial we suggest that you spend some time getting acquainted with the basics of R. We will be using R throughout the course.

The first classes are on January 8. Before you come to class do the following:

  1. Read through the course syllabus

  2. Read the R resources section of the course webpage. Make sure to login to http://rstudio.chass.utoronto.ca/ (see R resources section for more details).

  3. Sign up for the Piazza discussion forum.

  4. Get introduced to R. Two ways to get you started are:

  1. Complete Datacamp’s free online Introduction to R

  2. Read chapters 1, 2, and 3 of Hands-On Programming with R, by Garrett Grolemund.

You can do both (i) and (ii), but a lot of the same content is covered. If you decide to only complete the readings then make sure to type the commands into the console window in RStudio.

Week 1

January 8 Class

Class slides - Prof. Taback

Introduction to R script

R Markdown source of slides

Class slides - Prof. Gibbs

R Markdown source of slides

Happiness Datasets

Modern Data Science with R: Section 2.1 and chapter 3 up to and including section 3.2.2.

January 12 Tutorial

Practice problems

Example solutions to practice problems
Note: in question 1, the textbook asks for scatterplots of each person’s height against their father’s height. The x- and y-axes in the plots in the solutions should be switched.

Week 4

February 2 Tutorial

Practice Problems

Example solutions to practice problems
Typo in solution to Question 2 corrected on March 1. It used to say the test statistic is 0.38 in one spot, but the test statistic is 0.17, as used elsewhere else in the solution.

Week 5

February 5 Class

Slides and References

Note: A new version of the unannotated slides was posted February 8 (both html and pdf). This version corrects a few typos noted in class plus a typo on pages 58 and 59 (in the mathematical note that you’re not responsible for).

Class slides

Class slides

R Markdown source of slides

Annotated slides - 10:00 class

Annotated slides - 14:00 class

Introductory Statistics with Randomization and Simulation - Sections 2.1, 2.2, 2.3 (excluding 2.3.4)

Week 6

February 12 Class

Slides and References

Class slides (Watch for the typo on slide 46!)

Class slides (Watch for the typo on slide 46!)

R Markdown source of slides

Announcement about Mental Health Project

Annotated slides - 10:00 class

Annotated slides - 14:00 class

Modern Data Science with R: 7.1, 7.2, 7.3

Week 9

March 12 Class

Slides and References

Class slides

Class slides

Annotated slides - 10:00 class

Annotated slides - 14:00 class

R Markdown source of slides

Modern Data Science with R: page 189, page 465 - 468, page 470.

Geotab Data Scientist Brenda Nguyen’s presentation on Hazardous Driving Data

Week 11

March 26 Class

Annotated slides - 10:00 class

Annotated slides - 14:00 class

Class slides

Class slides

Modern Data Science with R: Chapter 6.

Slides and References

April 30 - No Tutorial ( University Closed on Good Friday)

Practice Problems

Example solutions to practice problems

Week 12

April 2 STA130 Poster Fair

Project Information

Tutorial Content

Documents created by TAs for tutorials can be found here.

R Markdown source