Course overview

An introduction to data science theory is provided with some focus on analytics. Topics covered include an introduction to R and other appropriate computational platforms, data types, data manipulation, data frames, data visualization, data reporting, statistical/machine learning, classification, clustering, cross-validation, classification and regression trees, gradient boosting, ridge regression, LASSO, and generalized additive models. Familiarity with some computer package, e.g., SAS, Python, or MatLab, is required. This course includes a scientific communication component.

Expected outcomes

Upon completion of this course, the student will be able to:

• use visualization tools to explore the data using R
• perform analysis using unsupervised and supervised learning methods
• analyze a real data set of moderate size using R and interpret the output
• write reusable data analysis reports using R, RStudio, and RMarkdown

Course Information

2 Lectures and 1 Lab.

Prerequisites

One of ECON 3EE3 or PNB 3XE3 or SFWRTECH 4DA3 or STATS 3A03.

Textbook

• Suggested textbooks:
  • ISLR: An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (Springer, 1st ed., 2013). - available at campus store.
  • RDS: R for Data Science by Garrett Grolemund and Hadley Wickham. - available online.
  • RMC: R Markdown Cookbook by Yihui Xie, Christophe Dervieux, Emily Riederer. - available online.

Software

This course uses R and RStudio, which are both free. We recommend to set-up the computing environment earlier. We will also use the first-week lab session to ensure everyone is up and running with the computing environment.

• Install the following software (follow the interactive tutorial to install R and R Studio: click here):
  • R (required): https://www.r-project.org/.
  • RStudio is highly recommended for syntax highlighting, package management, document generation, and more: https://www.rstudio.com/.
    • The newest version of RStudio is highly recommended.
  • Latex, which will enable you to create PDFs directly from the RMarkdown in RStudio.
    • Install TinyTex package: install.packages("tinytex", repos = "https://cloud.r-project.org/").
    • After installing TinyTex, close RStudio.
    • Reopen RStudio.
    • Run the following: tinytex::install_tinytex().

Evaluation

The homework assignments and bonus points will determine the final letter grade for this course:

• Six homework assignments will be assigned.
• The best five assignments will count towards your overall grade.
• Each of your five best assignments will be worth 20% of the final grade.

The final percentage to letter grade conversion will follow McMaster’s Grading Scale.

Course Schedule

R Markdown files

Available upon request.