Course Text Books

As introduced in the course syllabus, there are two text books required in this course. Other readings will be linked below.

Other course readings will be linked below or (when necessary) made available on Canvas.

Reading Schedule

It is expected that students will be prepared when they come to class. Being prepared includes doing the required reading listed below.

Week 01, Jan. 18: Intro. of Data Science, Getting to Know R

Lecture Slides

No readings. (Course introduction)

Week 02, Jan. 25: Data Storage and Data Types

Lecture Slides

In-Class Lab

Course Survey

W&G Chapter 1: Welcome - Introduction, Chapter 2: Explore - Introduction, Chapter 4 Explore: Workflow Basics

Additionally please be sure to have both R and R Studio installed on your machine before our class meeting.

Optional reading

Week 04, Feb. 08: Data Visualization

Lecture Slides

In-Class Lab

W&G Chapter 3: Data Visualization

Healy, Data Visualization: A Practical Introduction Chapters 1 and 3. (on Canvas)

Optional reading

Week 08, Mar. 15: Working with Regression

Lecture Slides

In-Class Lab

W&G Chapter 24: Model Building; Chapter 21: Iteration

G 3.3, 4.1–4.6

Optional reading

Week 09, Mar. 22: Working with Classification

Lecture Slides

In-Class Lab File

Kuhn, Max. 2019. caret. Chapter 5: Model Training and Tuning

G 5–6

Week 13, Apr. 19: TBD Week

Lecture Slides

In-Class Lab File

No readings. Optional resources to be shared during class session