MACS 30500 - Computing for the Social Sciences

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

Instructor: Benjamin Soltoff, Lecturer in Computational Social Science
Teaching Assistant: Joshua Mausolf
Meeting day/time: MW 1:30-2:50pm, Rosenwald Hall 405
Online course discussion: GitHub discussion repo
Open lab session: W 5-5:50pm, Saieh Hall of Economics 247
Office hours: Th 2-4pm (Saieh Hall of Economics 249)
TA office hours: Tu 1:30-2:50pm (Saieh Hall of Economics 248)
Prerequisites: None
Requirements: Bring your own laptop

Course Description

This is a for-credit, applied course for social scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on developing a computational framework which students can implement in their own research. Major emphasis is placed on a pragmatic understanding of programming languages and software libraries. Students will leave the course with basic computational skills in a wide variety of techniques and languages; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions and data.

From The Rise of Partisanship in the U.S. House of Representatives. The authors use network visualizations to depict ideological polarization over time.

Course Objectives

By the end of the course, students will:

Construct and execute basic programs in R and Python using elementary programming techniques (e.g. loops, conditional statements, user-defined functions)
Apply stylistic principles of coding to generate reusable, interpretable code
Debug programs for errors
Identify and use external libraries to expand on base functions
Generate reproducible research with R Markdown and Jupyter Notebooks
Implement statistical analysis
Visualize information and data using appropriate graphical techniques
Import data from files or the internet
Munge raw data into a tidy format
Scrape websites to collect data for analysis
Analyze networks
Parse and analyze text documents
Implement machine learning algorithms
Connect to and obtain data from relational databases through local and API access
Write programs which utilize parallel processing
Create interactive web pages using Shiny