FAQ

This content is from the fall 2016 version of this course. Please go here for the most recent version.

Textbooks/Readings

Required

R for Data Science – Garrett Grolemund and Hadley Wickham. Hardcover to be published late 2016, but an open-source online version is available now.
Think Python, 2nd Edition – Allen B. Downey. Hardcover available on Amazon for $40, but a digital copy is available via the UChicago library (authentication required).

Optional

ggplot2: Elegant Graphics for Data Analysis, 2nd Edition – Hadley Wickham. Excellent resource for the ggplot2 graphics library.
RStudio Cheatsheets - printable cheat sheets for common R tasks and features, such as data wrangling, data visualization, base R functions, and even the RStudio IDE itself.
Python for Data Analysis – Wes McKinney. Excellent companion for those who wish to use Python for data analysis. Covers similar topics to what we will be doing in R, through use of the NumPy, scipy, and pandas libraries. Downside is that it is written for Python 2, but most examples and code are still usable. Also getting a bit dated (written in 2012) but a second edition is expected in 2017. A digital copy is available via the UChicago library (authentication required).

Software

By Day 2, you should install the following software on your computer:

R - 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. You could use base R, but you would regret it.
Anaconda - Anaconda is a popular distribution of Python and includes many important packages already pre-loaded.
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.

Evaluation

Homework assignments (70%)

Each week students will complete weekly programming assignments linked to lecture materials. These assignments will be due the following week prior to Wednesday’s class. Weekly lab sessions will be held to assist students in completing these assignments. 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.

Final project (30%)

The final project will be an application of computational social science which incorporates concepts and methodological approachs from throughout the quarter. These projects are expected to tackle a relevant research question in the social sciences. Projects will be completed either individually or in a group of up to 3 students; the larger the group, the higher the expectations. Detailed instructions for the final project can be found here.

Statement on Disabilities

If you need any special accommodations, please provide me (Dr. Soltoff) with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) as soon as possible so that you may discuss with me how your accommodations may be implemented in this course.

This work is licensed under the CC BY-NC 4.0 Creative Commons License.