Syllabus

Professor: Jean Clipperton, clipperton@uchicago.edu, OH: Mon/T/W/Th 1:00-2:00 at this zoom link
TA: Sarthak Dhanke, sarthakdhanke@uchicago.edu, OH:daily, 4:00pm-5:30pm at this zoom link

Course Description

This is an applied course for social scientists with little-to-no programming experience who wish to harness growing digital and computational resource with interest in data science and/or computational social science. In this course, we will cover the programming language R, the importance of version control (Git and GitHub), and best practices for programming. By the end of the course, students will have worked through most of the basics in R as a programming language, completed assignments on the core components of it, be adept in using the tidyverse package, and have applied practice using data of their choice.

Course Objectives

By the end of the course, students will:

  • Construct and execute basic programs in R using elementary programming techniques and tidyverse. packages (e.g. loops, conditional statements, user-defined functions)
  • Learn the basics of Git, RStudio, and R markdown for computing.
  • Understand and incorporate packages within R.
  • Apply stylistic principles of coding to generate reusable, interpretable code
  • Apply Git and GitHub workflows for version control
  • Implement best practices for reproducible research
  • Visualize information and data using appropriate graphical techniques
  • Import data from files or the internet
  • Scrape websites to collect data for analysis

Books and Materials

Required. * R for Data Science (2e). – Garrett Grolemund and Hadley Wickham. We will be reading most chapters from this book. The open-source online version is available for free; the hardcover version available for purchase online.

Additional resources.

General Policies Academic Integrity

University education is predicated on original work and the intellectual integrity of the persons engaged in creative discovery. The University of Chicago is committed to maintaining a cooperative, open intellectual climate in which those who search for knowledge and understanding receive credit for their personal contributions. Accordingly, all students in this course are expected to abide by scholarly norms and University policies regarding academic integrity. These policies, and resources about best practices to employ in order to abide by them, are available through UChicago’s website. Violations of these standards, even if “unintentional,” may result in serious sanctions.

ChatGPT/CoPilot/LLMs/Generative AI

We are living in an exciting age! The advent of LLMs has transformed the educational experience and programming is now fundamentally different from prior educational experiences. It can be very tempting to use LLMs such as ChatGPT and you are permitted to do so in this class under the following conditions:

  1. You first attempt the assignment on your own
  2. You use AI to troubleshoot NOT in place of your own work
  3. You are capable of understanding what comes out of your model and you acknowledge that you are responsible for ALL work submitted in your assignments

Homework and AI statements

Every single assignment needs to include a statement about how/if you used AI in the completion of the assignment (if you did not use it, you can say so; if you used other sources, you can document them). For your assignment, you need to document how you sought help, the prompts you used, and how the model helped you move forward on your assignment.

In this particular class, you should aim to have ~95% of your work completed solo, with your first stop for assistance either your instructor or TA, then the web, and then ChatGPT / LLMs.

Important Note Just because AI is permitted does not mean that you can use it to complete your assignment for you. If you are suspected of using AI in some form to complete your assignment on your behalf, that will not be tolerated and you will be immediately reported for a violation of academic integrity. To be perfectly clear: any non-cited use of AI or external tools is an academic violation. GPT (and other AI tools) can be used but only as tools and not in place of reducing your effort or learning.

Access & Inclusion

Difference enhances both the teaching and learning experiences. The classroom is a space where all students are welcome, regardless of age, dis/ability, ethnicity, gender identity and/or expression, national origin, race, religious non/belief, sex, sexual orientation, socioeconomic status, and alignment with other identities or contexts. Furthermore, if any student has a particular consideration, including learning and participation style, that affects their ability to meet course expectations, please see me as soon as possible. I am personally committed to creating and maintaining an inclusive learning environment for each and every student. Please, do not hesitate to contact me with specific needs or concerns, and the sooner the better. Maintaining transparency (and communication in general) with your instructor is not only a good professional skill, but also a good way to develop a more one-on-one relationship. Furthermore, accommodations are far easier and effective to arrange when planned than when rushed. In short, I will make every effort to ensure students equal access. Please let me know how I can help make this class work for you.

My classroom is intended to be a constructive and critical space, wherein all students feel comfortable engaging openly with the material, each other, and oneself. However, this is only possible when everyone commits to this endeavor. I expect you to do so, and to help your peers (and me) to do the same. While I very much encourage (and celebrate) dissent and/or debate, I will not tolerate disrespect in my classroom. Please let me know if you feel the principles expressed in this syllabus are not being upheld so that I can address it as soon as possible.

Communication

I am generally available via email at the address above, and will do my best to respond within 24 hours of contact during the week and 48 hours on weekends–I try not to check email much on weekends FYI. In addition to the office hours above, there will likely be time at the end of each class meeting to discuss individual issues. Please do not hesitate to be in touch with any questions or concerns. It’s helpful for me if you put ‘MACS 30500’ in the heading. I do ask that you check the syllabus before contacting me because the answer you seek is most likely there already.

FAQ

What do I need for this course?

You will need a computer each day. Class sessions are a mix of lecture, demonstration, and live coding. It is essential to have a computer so you can follow along and complete the exercises. By the end of the first week, you should make sure you can access the following software: * R. - the easiest approach is to select a pre-compiled binary appropriate for your operating system.. * RStudio’s IDE (available via Posit). - this is a powerful user interface for programming in R. * Git. - Git is a version control system. which is used to manage projects and track changes in computer files. Once installed, it can be integrated into RStudio to manage your course assignments and other projects. Comprehensive instructions for downloading and setting up this software can be found here..

How will I be evaluated?

Students will complete a series of (roughly) weekly programming assignments linked to class materials.Each assignment will be evaluated by myself or a TA. Assignments will initially come with starter code, or an initial version of the program where you need to fill in the blanks to make it work. As the quarter moves on and your skills become more developed, less help upfront will be provided. While students are encouraged to assist one another in debugging programs and solving problems in these assignments, it is imperative students also learn how to do this for themselves. That is, students need to understand, write, and submit their own work.

Weekly topics:

  • Intro to computing
  • Visualizations/grammar of graphics
  • Data transformation
  • Exploratory data analysis
  • Pipes and functions
  • Reproducible workflow
  • Version control
  • Web scraping
  • Text analysis

Acknowledgments

• This page has been developed starting from Benjamin Soltoff’s “Computing for the Social Sciences” course materials, licensed under the CC BY-NC 4.0 Creative Commons License and building on materials developed by Sabrina Nardin. This version of the course also contains novel assignments and slides created by Jean Clipperton.