Lecture and session attendances are mandatory. Participation (20%) is a significant part of learning for Gov 51 and we will try our best to make your time worth it. You can skip 1 lecture and 1 section without excuses. You can submit 1 problem set late without any excuses.

We will mainly reference QSS: An Introduction as our textbook. It is a great textbook for quantitative methods in social science. We will also read tutorials and chapters from other textbooks for certain topics.

Moreoever, Gov 51 will closely follow Gov 50. Gov 50 is the recommended (but not required) pre-requisite for this class. Please see a list of helpful resources by the amazing Prof. Matt Blackwell.

Schedule

Date	Topic	Readings	Materials	Code
Observational Causal Inference
Week 1
Jan 28, 30	Review	QSS Chapter 2 - Causality	Lec1 | Lec2 |	Data | Code | Answer
Jan 30	Section 1		Section1 (Sima) | Section1 (Pranav)
Week 2
Feb 4, 6	Instrumental Variables	IV Introduction | Mastering Metrics | Mostly Harmless Chapter 4 (Optional)	Lec3 | Lec4	code | data
Feb 6	Section 2		Section2 (Sima) | Section2 (Pranav)
Week 3
Feb 11, 13	Matching	Sample Balancing | Lecture by Gary King (optional) | Matching Estimators (optional)	Lec5 | Lec6	code
Feb 13	Section 3		Section3 (Sima) | Section3 (Pranav)
Regression and Prediction
Week 4
Feb 18, 20	Review on OLS	QSS Chapter 4 - Prediction	Lec 7 | Lec 8	Election_2016.Rmd | county data | 2012 | 2016 | Answer
Feb 20	Section 4		Section4 (Sima) | Section4 (Pranav)
Problem Set I: 10% due on 2/20 11:59pm
Week 5
Feb 25, 27	Penalized Regressions and Variable Selection	Penalized Regression | Chapter 3: Regression Approaches | ISL Chapter 6 (First Edition) (Optional)	Lec9 | Lec10	Lasso
Feb 27	Section 5		Section5 (Sima) | Section5 (Pranav)
Week 6
Mar 4, 6	Uncertainty and Inference	QSS Chapter 6 - Probability and Chapter 7 - Uncertainty	Lec11 | Lec12	Code | Data | Answer
March 6	Section 6		Section6 (Sima) | Section6 (Pranav) | Midterm Topics (Pranav)
Week 7
Mar 11,13	Review and Midterm Exam		Review Session	-
Week 8: Spring break
Week 9
Mar 25, 27	Missing Data in Multiple Regression	Statistical Analysis with Missing Data - Chapter 1: Introduction | Missing Data by Gary King (Optional)	Lec14 | Lec15	Code
March 27	Section 7		Section7 (Sima) | Section7 (Pranav)
Problem Set II: 10%
Machine Learning Methods
Week 10
Apr 1, 3	Text as Data: Bag of Words	Supervised machine learning for text analysis in R (Chapter 1 and 2) | NLP with Google (Ignore the coding part)	Lec16 | Lec17	Sentiment analysis | Newspaper_code | Newspaper_data
April 3	Section 8		Section8 (Sima) | Section8 (Pranav) |
Week 11
Apr 8, 10	Text as Data: Unsupervised Learning	Unsupervised learning | Text and causal inference (optional) | Text preprocessing for unsupervised learning (optional)	Lec18	Topic Modelings | Key ATM (at the bottom of the code)
Apr 10	Section 9		Section9 (Sima) | Section9 (Pranav)
Week 12
Apr 15, 17	Black Box Prediction Methods	Machine Learning by Andrew Ng (Skip the first 35 min during which he talks about logistics of the class.)	Lec19	Tree_Forest
Week 13
Apr 22, 24	Network Analysis	QSS Chapter 5 - Discovery	Lec20	code 1 | code 2 | data
Problem Set III: 10%
Week 14
Apr 29	Poster Session		-	-
Problem Set III: 10%

Computing

We’ll use R in this class to conduct data analysis. R is free, open source, and available on all major platforms (including Solaris, so no excuses). RStudio (also free) is a graphical interface to R that is widely used to work with the R language. You can find a virtually endless set of resources for R and RStudio on the internet. For beginners, there are several web-based tutorials. In these, you will be able to learn the basic syntax of R. We’ll post more R resources on the course website.

Accessibility

Harvard University values inclusive excellence and providing equal educational opportunities for all students. Our goal is to remove barriers for disabled students related to inaccessible elements of instruction or design in this course. If reasonable accommodations are necessary to provide access, please contact the Disability Access Office (DAO). Accommodations do not alter fundamental requirements of the course and are not retroactive. Students should request accommodations as early as possible, since they may take time to implement. Students should notify DAO at any time during the semester if adjustments to their communicated accommodation plan are needed.

Mental Health

College is a stressful time in one’s life and mixing it with a global pandemic, remote learning, and dislocation makes this one of the most fraught time any of us have probably faced. Please just get in touch if you are in need of support. Of course, there are other resources at Harvard if you need them. A few are listed below:

• Counseling and Mental Health Services
• We are all human at Harvard