Syllabus
Instructor
- Juan F. Tellez
- Kerr Hall 583
- jftellez@ucdavis.edu
- Meet with Prof. Tellez
Teaching Assistants
- Rio Park, Kerr 677
rgpark@ucdavis.edu
OH: Wed, 10AM-12PM - Alexa Federice, Kerr 675
afederice@ucdavis.edu
OH: Tu, 11AM-1PM - Jack Rametta, Kerr 566
jtrametta@ucdavis.edu
OH: TH, 9AM-11AM
Course details
- T,TH
- Sept 22 - Dec 1, 2022
- 3:10 PM - 4:30 PM
- Everson Hall 176
Contacting us
E-mail and Slack are the best ways to get in contact with your instructors. We will try to respond to all course-related e-mails and Slack messages quickly, but also remember that life can be busy and chaotic for everyone (including me!)
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 data science, programming, regression, causal inference, and uncertainty.
This course assumes students have no statistics or programming background. We will emphasize hands-on application with software and intuition-building instead of statistical theory and mathematical proofs.
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. There are also no exams.
Instead, you will spend time getting your hands dirty with data in weekly problem sets touching on the class content. You will also complete an original data analysis project on a topic of your choosing that you will showcase at the end of the quarter.
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:
- Feel comfortable manipulating data in R
- Craft effective visualizations of patterns in data
- Draw causal diagrams and identify obstacles to causal claims
- 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, and the final project. 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
- Get help from the TAs on your final project
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. You will use RStudio as the main program to access R.
Given the focus on programming, you will need consistent access to a laptop or computer for this class.
Course Policies
How can I get help or get in contact with 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.
If you need one-on-one help you can also reach out to the TAs to schedule a time to meet. The TAs and their office hours are 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), you can sign up for office hours on Calendly. 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.
Honor Code
Be nice. Don’t cheat. The Code of Academic Conduct is in effect in this class and all others at the University. I will treat violations seriously. If you have doubts, it is your responsibility to ask about the Code’s application.
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 | 15% |
| Problem sets (8) | 50% |
| Final project | 35% |
| 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 an old computer, something with a cyberpunk aesthetic, or some old technology to the Slack (I’ll round your final grade up to the nearest whole number; you’ve got until September 29th).
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, and others who have made their courses publicly available.