Approaches for developing, analyzing, and managing large datasets for computational social science. Development topics include acquiring data from the web and log files and managing unstructured data. Analysis approaches overviewed include econometric, social network, text, and sequence analysis. Data management topics cover sharing, protecting, and archiving data.

Oasis Title

Big Data Research

Semester Course Offered

Offered every year.

Grading System

A-F (Traditional)

Course Objectives

Students will survey the different computational social science approaches for analyzing large datasets and will gain hands-on skills and basic understanding of tools for developing and managing these datasets.

Topical Outline

Topical Outline:

  1. Introduction: What is big data in the context of social science research? What is computational social science?

  2. Developing large datasets: Gain an understanding in working with structured versus unstructured data, learn methods for trace data extraction and development techniques (web scraping, log files, multimedia files, etc.)

  3. Survey of social science approaches: Introduce and compare different methods for analyzing big data, including econometrics, social network analysis, sequence analysis, text analysis, etc. (note, this is an introduction and comparison of these approaches -รข€“ instructors would point to other classes in Terry and throughout UGA for more in-depth understandings). Includes introduction to popular tools for analyzing and visualizing large datasets.

  4. Data management: Topics include storing, sharing, protecting, and archiving data and the development of data management plans.

Syllabi MIST 9777
Credit Hours 3.0