Schedule

Below is the schedule for the semester. You can find the materials for each course meeting under the “Content” links for that week. You should generally:

• watch the lecture videos (if any) and complete the readings by Monday;
• complete the tutorials by Monday evening at 11:59pm; and
• submit the problem set or exam by Wednesday at 11:59pm.

Here’s a guide to the schedule:

• Materials (): This page contains the readings, slides, and recorded lectures (if any) for the topic. Read/watch these first.
• Tutorial (): A link to the tutorial for that week.
• Assignment (): This page contains the instructions for each assignment. Assignments are due by 11:59 PM on the day they’re listed.

The readings refer to following texts:

• QSS: Quantitative Social Science: An Introduction in tidyverse by Kosuke Imai and Nora Webb Williams
• MD: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse by Chester Ismay and Albert Y. Kim
• IMS: Introduction to Modern Statistics by Mine Çetinkaya-Rundel and Johanna Hardin.

Date	Title	Reading	Materials	Tutorial	Assignment
Week 1
September 5	Introduction to the course
September 7	R, Rstudio, and data visualization	MD Ch 1
Week 2
September 11	Tutorial 1  (submit by 23:59:00)	MD Ch 2
September 12	Data visualization
September 13	Tutorial 2  (submit by 23:59:00)
September 14	Data wrangling	MD Ch 3
Week 3
September 18	Tutorial 3  (submit by 23:59:00)	QSS 2.1-2.2
September 19	Data wrangling
September 20	Problem Set 1  (submit by 23:59:00)
September 21	Causal inference	QSS Ch 2.3
Week 4
September 26	Causal inference and randomized experiments	QSS 2.4
September 27	Problem Set 2  (submit by 23:59:00)
September 28	Causal inference and observational studies	QSS 2.5
September 29	Final Project Milestone 1  (submit by 23:59:00)
Week 5
October 2	Tutorial 4  (submit by 23:59:00)	QSS Ch 2.6-3.3
October 3	Summarizing data
October 4	Problem Set 3  (submit by 23:59:00)
October 5	Survey sampling	QSS 3.4
Week 6
October 9	Tutorial 5  (submit by 23:59:00)	QSS Ch 3.5-3.6 or MD Ch 4
October 10	Summarizing relationships in our data
October 11	Problem Set 4  (submit by 23:59:00)
October 12	Importing and joining data
October 13	Final Project Milestone 2  (submit by 23:59:00)
Week 7
October 16	Tutorial 6  (submit by 23:59:00)	QSS 4.1 (except 4.1.2)
October 17	Midterm review & prediction
October 18	Regression	MD Ch 5 or QSS 4.2.1-4.2.4
October 19–October 22	Exam 1  (submit by 23:59:00)
Week 8
October 24	Regression and model fit	MD Ch 6.1-6.2 or QSS 4.2.6-4.3.2
October 26	Multiple regression and interpretation
Week 9
October 30	Tutorial 7  (submit by 23:59:00)	MD Ch 7
October 31	Sampling
November 1	Problem Set 5  (submit by 23:59:00)
November 2	Sampling distributions
November 3	Final Project Milestone 3  (submit by 23:59:00)
Week 10
November 6	Tutorial 8  (submit by 23:59:00)	MD Ch 8/IMS Ch 12
November 7	The bootstrap and confidence intervals
November 8	Problem Set 6  (submit by 23:59:00)
November 9	The bootstrap and confidence intervals
Week 11
November 14	Hypothesis testing	MD Ch 9/IMS Ch 11
November 15	Problem Set 7  (submit by 23:59:00)
November 16	Hypothesis testing
November 17	Final Project Milestone 4  (submit by 23:59:00)
Week 12
November 22	Hypothesis testing
Week 13
November 28	Mathematical models of uncertainty	IMS Ch 13
November 29	Problem Set 8  (submit by 23:59:00)
November 30	Mathematical models of uncertainty
Week 14
December 5	Uncertainty in regression	QSS Ch 7.3
December 7–December 10	Exam 2  (submit by 23:59:00)