Course Information
MACS 20500/30500 (SOCI 20278;40176; ENST 20550; PLSC 30235; MAPS 30500; CHDV 30511)
5 Weeks
June 22 – July 24, 2020
M-T-W-Th 9:30-11:30am

100 units

Instructor: Benjamin Soltoff, Computational Social Science

Course Description: This is an 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 generating reproducible research through the use of programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to social science; 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, methods, and data. More information can be found at https://cfss.uchicago.edu

Goals and 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)
  • 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
  • Implement statistical learning algorithms
  • Utilize cross validation methods
  • 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
  • Parse and analyze text documents
  • Implement programs via distributed computing platforms
  • Create interactive web pages using flexdashboard and Shiny

Course instruction will be in the classroom via lecture and live-coding exercises in the main lecture and lab sessions. The course implements a flipped classroom design, enabling students to receive direct feedback and instruction when solving problems and debugging code.

Degree Requirement Completion
This course:

• Is an approved elective in the Environmental and Urban Studies major and minor
• Satisfies the methods requirement in the Public Policy Studies major
• Is an approved elective in the Latin American and Caribbean Studies major

Course Information
GEOG 28702/38702 (SOCI 20283/30283; ENST 28702)
5 Weeks
June 22 – July 24, 2020
M-T-W-Th 1:30-3:00pm

100 units

Instructor: Kevin Credit, Center for Spatial Data Science

Course Description: If you’ve ever been interested in learning more about spatial analysis or getting a geographic twist in computational thinking, this is the course to take. You may be interested in working with new types of spatial data to enhance a project in social science, economics, public health, crime, etc. You may be interested in learning some applied coding, or extending the programming or statistical skills you already have. Or, you may just be curious in thinking about the world in a different way. The spatial perspective is a powerful conceptual and technical scientific approach that facilitates new ways of viewing the world.

This course provides an overview of how spatial thinking is translated into specific methods to handle geographic information and statistical analysis, with a focus on research questions relevant in the social sciences. Basics of cartography, spatial data wrangling, and the essential elements of spatial analysis are introduced within this context. Examples include spatial data integration (spatial join), transformations between different spatial scales (overlay), the computation of “spatial” variables (distance, buffer, shortest path), geovisualization, visual analytics, and the assessment of spatial autocorrelation (the lack of independence among spatial variables). The methods will be illustrated by means of open source software such as QGIS and R; this course does not teach a specific GIS software program.

Goals and Objectives: We’ll be using the R programming language and additional open source software packages to learn and practice spatial analysis, and use various (old and new) types of data in applied labs to put newly learned concepts to the test. Favorite labs include working with raw crime data from multiple U.S. cities; learning about how coal mining impacts West Virginian towns across time; and developing and visualizing a bike network using millions of Divvy data points. We’ll also visit the Oriental Institute to see how ancient cartographic techniques still remain fresh and modern today.

Degree Requirement Completion
This course:

• Is a required course for Environmental and Urban Studies major, and approved elective course for minor
• Satisfies the methods requirement in the Public Policy Studies major
•  Is an approved elective course for the Geographic Information Science minor
• Is an approved elective for the Latin American and Caribbean Studies major

Course Information
PSYC 20200
5 Weeks
June 22 – July 24, 2020

100 units

Instructor: Colin Quirk, Department of Psychology

Course Description: This course introduces concepts and methods used in behavioral research. Topics include the nature of behavioral research, testing of research ideas, quantitative and qualitative techniques of data collection, artifacts in behavioral research, analyzing and interpreting research data, and ethical considerations in research.

Degree Requirement Completion
This course:

• Is a required course for Psychology majors.

Course Information
SOSC 20224/30224 (ANTH 21432; ANTH 31432; ENST 20224; GLST 26220; SOCI 20515; SOSC 30224)
5 Weeks
June 22 – July 24, 2020
M-W-F 9:30-11:30am

100 units

Instructor: Benjamin Fogarty-Valenzuela, Department of Sociology

Course Description
“Virtual worlds are places of imagination that encompass practices of play, performance, creativity and ritual.” – Tom Boellstorff, from Ethnography and Virtual Worlds: A Handbook of Method

This course is designed to provide students in the social sciences with a review of ethnographic research methods, exposure to major debates on ethnographic research, opportunities to try their hand at practicing fieldwork virtually, and feedback on a proposed study that employs ethnographic methods. By way of analyzing and problematizing enduring oppositions associated with ethnographic fieldwork – field/home, insider/outsider, researcher/research subject, expert/novice, ‘being there’/removal – this seminar is a practicum in theoretically grounded and critically reflexive qualitative methods of research. By introducing students to participant observation and interviews in virtual worlds, ethics, data analysis and writing up, the course offers an opportunity to make sense of the current pandemic we’re all experiencing in real time. An emphasis will be placed on multimedia, digital, and virtual ethnography. 

Degree Requirement Completion
This course:

• Satisfies the methodology course requirement for the Sociology major.
• Is an approved elective course for the ENST major and minor requirements (must petition with relevance to course of study).
• Satisfies the methodology course requirement for the Anthropology major.
• Counts as a methodology elective for the Global Studies major.
• Is an approved elective for the Latin American and Caribbean Studies major.
• Is an approved methodology course in the Health and Society minor.

Course Information
MACS 24000 / MACS 34000
2 Weeks
M-T-W-Th-F 10-11:30am (lunch break); 1-2:30pm

100 units

Instructors: Benjamin Soltoff, Computational Social Science, Philip Waggoner, Computational Social Science

Course Description:

This course introduces students to techniques for extracting and communicating knowledge from data. In the first half, students study visualizations as a method for summarizing information and reporting analysis and conclusions in a compelling format. This introduces the ideas and methods of data visualization, with emphasis on both why you are doing something as well as how to produce optimal visualizations. In the second half, students are introduced to the rapidly developing world of data mining. Focus will be on knowledge discovery and pattern recognition in the context of social science problem solving. From partitioning and anomaly detection to text clustering, high-dimensional mining, and deep learning, students will be given a thorough introduction to prominent techniques for exploring and discovering patterns in data. Throughout the course, class sessions will combine lecture, coding challenges, and computational problem solving to encourage wide engagement with the techniques using the R programming language.

Pre-Requisites: MACS 20500. STAT 23400 or similar introductory statistics course is expected. Experience in R required.
Necessary concepts of Skills CS 10121 or a similar introductory programming course. Experience with machine learning is helpful but not required.