Data 101: Making Predictions with Data

Welcome

All of the course material can also be accessed through Canvas. The syllabus can be found in the Syllabus tab in the Navigation bar.

Lectures

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Schedule

Lecture Topic Supplementary Reading
1 Welcome! Introduction To R and RStudio
2 Getting Familiar with R
3 R Programming: Comparison and Logical Operators, Conditionals: (e.g. if statements, else if statements), base R wrangling (e.g. conditional indexing), Loops
4 Getting data into R: File formats and location, Functions for reading data into R (read.csv() and read_csv()), Working Directories (absolute vs. relative paths, Dates and Times, the tidyverse package. Peng (2016) Ch 11
Timbers et al. (2022) Section 1.5
Wikham et al. (2023) Ch 6, 8
5 Data Wrangling with the dplyr functions: select, filter, arrange, rename, mutate, transmute, group_by, summarize and the piping operator (%>% and |>)
6 Data Wrangling Part 2 vignette("pivot") , Timbers et al. Chapter 3
7 Plotting with base R
8 Data Visualization with ggplot2

Timbers et al. Chapter 4

Extra resources: R graphics cookbook, posit basics,
9 Classification with k-nearest neighbours (KNN) Timbers et a. Chapter 5
10 Fitting and assessing KNN using tidymodels Timbers et a. Chapter 5/6.1-6.5
Review Session 2
11 Cross-validation Timbers Ch. 6
12 KNN regression Timbers Ch. 7
Review Session 3
13 Linear Regression Timbers Ch. 8
14 Clustering [R code] Timbers Ch. 9, tidymodels.org
Review Session 4

Labs

Lab number Topics Solutions
Lab 4/Assignment 3 Data wrangling + ggplot
Lab 5/Assignment 4 Classification
Lab 6/Assignment 5 Classification II: Evaluation + Tuning
Lab 7/Assignment 6 Regression Assignment 6
Lab 8 Linear Regression

References

Footnotes

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