| Course ID: | MIST 9777. 3 hours. 3 hours lecture and 0-1 hours lab per week. |
| Course Title: | Big Data Research |
Course
Description: | 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 -– 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. |