This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses and the timeline of topics and assignments might be updated throughout the semester.

week

dow

date

what

topic

prepare

slides

ae

ae_sa

hw

hw_sa

lab

lab_sa

exam

project

notes

1

M

Aug 29

Lab

No lab

T

Aug 30

Lec 1

Welcome to STA 199

Th

Sep 1

Lec 2

Meet the toolkit

2

M

Sep 5

Lab 0

Hello R!

Release: Lab 0

T

Sep 6

Lec 3

Grammar of graphics

Th

Sep 8

Lec 4

Visualizing various types of data

Release: HW 1

F

Sep 9

Due: Lab 0 + AE 1

Su

Sep 11

Due: AE 2

3

M

Sep 12

Lab 1

Data visualization

Release: Lab 1

T

Sep 13

Lec 5

Grammar of data wrangling

Th

Sep 15

Lec 6

Working with multiple data frames

Due: HW 1 / Release: HW 2

F

Sep 16

Due: Lab 1

4

M

Sep 19

Lab 2

Data wrangling

Release: Lab 2

T

Sep 20

Lec 7

Tidying data

Th

Sep 22

Lec 8

Data types and classes

Due HW 2

F

Sep 23

Due: Lab 2

5

M

Sep 26

Lab 3

Data tidying

Release: Lab 3

T

Sep 27

Lec 9

Importing and recoding data

Th

Sep 29

Lec 10

Exam 1 Review

Release: Exam 1 at 12pm

F

Sep 30

Due: Lab 3

6

M

Oct 3

Lab

No lab - Work on Exam 1

Due: Exam 1 at 2pm

T

Oct 4

Lec 11

Data science ethics - Misrepresentation

Th

Oct 6

Lec 12

Data science ethics - Algorithmic bias + data privacy

Release: HW 3

7

M

Oct 10

Lab

No lab - Fall break

T

Oct 11

Lecture

No Lec - Fall break

Th

Oct 13

Lec 13

Web scraping

Due: HW 3

8

M

Oct 17

Lab

Work on project proposal

T

Oct 18

Lec 14

Functions + iteration

Th

Oct 20

Lec 15

The language of models

F

Oct 21

Due: Project proposal

9

M

Oct 24

Lab 4

Probability + Simpson's Paradox

Release: Lab 4

T

Oct 25

Lec 16

Models with a single predictor

Th

Oct 27

Lec 17

Models with multiple predictors

Release: HW 4

F

Oct 28

Due: Lab 4

10

M

Oct 31

Lab 5

Predicting a numerical outcome

Release: Lab 5

T

Nov 1

Lec 18

Models with multiple predictors + Overfitting

Th

Nov 3

Lec 19

Logistic regression

Due: HW 4 / Release: HW 5

F

Nov 4

Due: Lab 5 / Release: HW 6

S

Nov 5

Due: Team peer evaluations 1

11

M

Nov 7

Lab

Work on project draft

T

Nov 8

Lec 20

Quantifying uncertainty with bootstrap intervals

Th

Nov 10

Lec 21

Hypothesis testing via simulation

Due: HW 5

F

Nov 11

Due: Project draft 1

12

M

Nov 14

Lab 6

Prediction + Bootstrapping

Release: Lab 6

T

Nov 15

Lec 22

Inference overview

Th

Nov 17

Lec 23

Exam 2 Review

Release: Exam 2 at 12pm

F

Nov 18

Due: Lab 6

13

M

Nov 21

Lab

No lab - Work on Exam 2

Due: Exam 2 at 2pm

T

Nov 22

Lec 24

No lecture - Work on projects

Th

Nov 24

Lecture

No lecture - Thanksgiving

F

Nov 25

Release: Exam retake (optional)

Su

Nov 27

Due: Project draft 2 (optional)

14

M

Nov 28

Lab

Work on project peer review

Due: Project peer review

T

Nov 29

Lec 25

Communicating data science results effectively

Due: Team peer evaluations 2

Th

Dec 1

Lec 26

Customizing Quarto reports and presentations

15

M

Dec 5

Lab

Project presentations

T

Dec 6

Lec 25

Looking further: Text analysis

Th

Dec 8

Lec 26

Looking further: Interactive web applications with Shiny