General Questions & Info

General description

This course is open to any graduate (or advanced undergraduate) at the University of Chicago. We draw from a wide range of departments such as Information Science, Sociology, Psychology, Political Science, etc. Typically these students are looking to learn basic computational and analytic skills they can apply to master’s projects or dissertation research.

If you have never programmed before or don’t know what the shell is, prepare for a different way of approaching problems. This class will prove to be very beneficial if you stick with it, but that will require you to commit for the full quarter. I do not presume any prior programming experience, so everyone starts from the same knowledge level. I guarantee that the first few weeks and assignments will be rough - but the good news is that they will be rough for everyone! Your classmates are struggling with you and you can lean on one another to get through the worst part of the learning curve.

Textbooks/Readings

Required

• R for Data Science – Garrett Grolemund and Hadley Wickham. We will be reading several chapters from this book. The open-source online version is available for free; the hardcover version available for purchase online.

Additional resources

• ggplot2: Elegant Graphics for Data Analysis, 3rd Edition – Hadley Wickham. Excellent resource for the ggplot2 graphics library.
• Advanced R – Hadley Wickham. A deep dive into R as a programming language, not just a tool for data science.
• An Introduction to Statistical Learning: with Applications in R – Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. A thorough introduction to statistical learning and machine learning methods, focusing on the fundamentals of how these methods work and the assumptions that go into them. See also ISLR tidymodels Labs. This site demonstrates how to implement all the labs.
• RStudio Cheatsheets - printable cheat sheets for common R tasks and features.

What do I need for this course?

We will program every day Class sessions are a mix of lecture, demonstration, and live coding. Be prepared to follow along and be engaged.

By the end of the first class, 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 - 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.

For further information see: * Evaluation criteria for homework * How to properly ask for help

–> –> –>