Syllabus

Instructor

Teaching assistants

  •   Alexa Federice (A01-A02)
      W 1pm-3pm
      Kerr 675
      afederice@ucdavis.edu
  •   Amanda Loehrke (A03-A04)
      T 9:45am-11:45am
      Kerr 566
      aloehrke@ucdavis.edu
  •   Richard Kornrumpf (A06-A07)
      F 12pm-2pm
      Kerr 567
      rlkornrumpf@ucdavis.edu
  •   Haley Daarstad (A05-A08)
      Th 10am-12pm
      Kerr 569
      hbdaarstad@ucdavis.edu

Course details

  •   Mon/Wed
  •   09/25-12/06
  •   8:00AM-9:20AM
  •   Young Hall 198

What is this class about?

Policymakers, academics, journalists, firms, NGOs and many others use quantitative data every day to make decisions. Data are also being used to make causal claims about the world – to argue that some policy will improve or worsen our lives.

This undergraduate course is about how to do data analysis and how to think causally about the political world. We will cover topics in political data science, causal inference, and uncertainty.

Note

This course assumes students have no statistics or programming background.

What will we do in this class?

Our class philosophy is all about doing. Readings are optional – everything you need to succeed in class is covered in lecture and section. Instead, you will spend time getting your hands dirty with data in weekly problem sets touching on the class content.

You will do this all in R, a powerful and in-demand programming language that will allow you to manipulate, summarize, and visualize the data that you care about. You will also develop a conceptual language for determining whether, and how, we can know that one thing causes another using data.

By the end of this course, you will be able to:

  1. Feel comfortable manipulating data in R
  2. Craft effective visualizations of patterns in data
  3. Draw causal diagrams and identify obstacles to causal claims
  4. Understand the basics of regression and uncertainty

What will we do in discussion section?

The weekly discussion sections will be the place to get help on lecture content, problem sets, etc. In section, you will:

  • Go over student questions from the week’s lecture content
  • Get help from the TAs on new problem sets and review answers from past problem sets

What materials do I need for this course?

All materials for this course are free and online. You will do all of your analysis with the open source (and free) programming language R and RStudio.

Given the focus on programming, you will need consistent access to a laptop or computer for this class.

Note

Follow the Installation Guide to get set up with R and RStudio

How can I get help or contact the instructors?

Slack will be our main mode of communication. We have a class Slack channel where anyone in the class can ask questions and anyone can answer. Ask questions about coding (e.g., “how do I summarize multiple variables at once?”) or class logistics (e.g., “I can’t find the reading”) in the class Slack workspace.

The TA’s and I will monitor Slack regularly, and you should all do so as well. You’ll have similar questions as your peers, and you’ll likely be able to answer other peoples’ questions too.

Discussion section and our office hours are other great places to get help. Our office hours are listed at the top of this page.

If you would like to speak with me about something that only pertains to you (e.g., your grades, academic advice) or need help, you should come to my office hours. If there’s a time-sensitive issue you can email me. Everything else goes in the Slack so that others can see and access help.

Counseling & Psychiatry Services

Life at Davis can be complicated and challenging. You might feel overwhelmed, experience anxiety or depression, or struggle with relationships or family responsibilities. UC Davis Counseling Services provide confidential support for students who are struggling with mental health and emotional challenges. Please do not hesitate to contact them for assistance—getting help is a smart and good thing to do.

Assignments and grades

You can find descriptions for all the assignments on the assignments page.

Assignment Percent
Weekly check-in 10%
Problem sets (8) 50%
Midterm exam 20%
Final exam 20%
Total 100%
Grade Range Grade Range
A 93–100% C 73–76%
A− 90–92% C− 70–72%
B+ 87–89% D+ 67–69%
B 83–86% D 63–66%
B− 80–82% D− 60–62%
C+ 77–79% F < 60%

Old tech

Once you have read this entire syllabus and the assignments page, please post a picture or gif of a technology that you was obsolete by the time you were an adult on Slack. I’ll round your final grade up to the nearest whole number; you’ve got until the end of week 2 of class.

Credits

This course draws on code, content, ideas, inspirations and much more from work by Andrew Heiss, Nick C. Huntington-Klein, Kieran Healy, Scott Cunningham, Richard McElreath and others who have made their courses publicly available.