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 0
September 1	Introduction to the course
Week 1
September 6	R, Rstudio, and data visualization	MD Ch 1-2
September 7	Tutorial 1  (submit by 23:59:00)
September 8	Data visualization
Week 2
September 12	Tutorial 2  (submit by 23:59:00)
September 13	Data wrangling	MD Ch 3
September 14	Problem Set 1  (submit by 23:59:00)
September 15	Data wrangling
Week 3
September 19	Tutorial 3  (submit by 23:59:00)
September 20	Causal inference and randomized experiments	QSS Ch 2.1-2.5
September 21	Problem Set 2  (submit by 23:59:00)
September 22	Causal inference and observational studies
Week 4
September 26	Tutorial 4  (submit by 23:59:00)
September 27	Summarizing data	QSS Ch 2.6-3.4
September 28	Problem Set 3  (submit by 23:59:00)
September 29	Survey sampling
Week 5
October 3	Tutorial 5  (submit by 23:59:00)
October 4	Summarizing relationships in our data	QSS Ch 3.5-3.6
October 5	Problem Set 4  (submit by 23:59:00)
October 6	Importing and joining data	MD Ch 4
Week 6
October 11	Prediction & Iteration	QSS 4.1 (except 4.1.2)
October 12	Tutorial 6  (submit by 23:59:00)
October 13	Regression	MD Ch 5 or QSS 4.2 (except 4.2.5)
October 13–October 16	Exam 1  (submit by 23:59:00)
Week 7
October 18	Multiple regression	MD Ch 6.1-6.2 or QSS 4.3.1-4.3.2
October 20	Interpreting regression
October 21	Final Project Milestone 1  (submit by 23:59:00)
Week 8
October 24	Tutorial 7  (submit by 23:59:00)
October 25	Sampling	MD Ch 7
October 26	Problem Set 5  (submit by 23:59:00)
October 27	Sampling distributions
October 28	Final Project Milestone 2  (submit by 23:59:00)
Week 9
October 31	Tutorial 8  (submit by 23:59:00)
November 1	The bootstrap and confidence intervals	MD Ch 8/IMS Ch 12
November 2	Problem Set 6  (submit by 23:59:00)
November 3	The bootstrap and confidence intervals
Week 10
November 8	Hypothesis testing	MD Ch 9/IMS Ch 11
November 9	Problem Set 7  (submit by 23:59:00)
November 10	Hypothesis testing
November 11	Final Project Milestone 3  (submit by 23:59:00)
Week 11
November 15	Hypothesis testing
November 16	Problem Set 8  (submit by 23:59:00)
November 17	Mathematical models of uncertainty	IMS Ch 13
November 18	Final Project Milestone 4  (submit by 23:59:00)
Week 12
November 22	Mathematical models of uncertainty
Week 13
November 29	Uncertainty in regression	QSS Ch 7.3
December 1	Review Session with TFs
December 1–December 4	Exam 2  (submit by 23:59:00)
Week 14
December 14	Final Project due  (submit by 23:59:00)