The Master of Science in Applied Quantitative Methods and Social Analysis is an interdisciplinary, flexible, and innovative degree that focuses on quantitative research methods for social analysis strategies and techniques. The program integrates the interdisciplinary perspectives and methodological and analytical tools across the College of Social Sciences and Humanities. The program seeks to educate ambitious social scientists and analysts primed to deploy computational tools for social analysis and tackle social science questions of equity, hierarchy, social organization, and social systems. The 21st-century economy will increasingly demand a workforce capable of collecting, processing, analyzing, and interpreting large-scale data on human attributes, personal preferences, social attributes, and political behavior. In response, this program provides students with rigorous training in quantitative research and social science methods to address important questions of social inquiry. Emphasizing public dissemination of findings, the program is designed to prepare students to inform policymakers, decision makers in the private and public sectors, and the broader community. These skills prepare graduates to pursue analytical or research careers in corporations, nonprofits, and public services or to continue their education.
Students in this degree program will have the opportunity to gain advanced training in statistical analysis and research methodology aligned to key areas of strength in CSSH, including data analytics in the social sciences, computational social science, network analysis in the social sciences, statistical methods in the social sciences, information ethics for social analysis, geospatial analysis, and the digital humanities. Students will also have the opportunity to stack a range of graduate certificate programs into the master’s degree.
The program will take advantage of various co-op opportunities—positions such as policy analysts, network scientists, econometricians, and crime analysts—that provide students with a professional environment to integrate quantitative skills and social analysis. The learning opportunities in professional settings (private sector, government, or nonprofit sector) reinforce the development of advanced quantitative skills and their applied nature to contemporary social issues. Ultimately, the Master of Science in Applied Quantitative Methods and Social Analysis will position students to enter the labor force with the competitive advantage of these experiences and skills.
CSSH Graduate Programs General Regulations
Complete all courses and requirements listed below unless otherwise indicated.
Required courses for this program may be offered at multiple campuses and through various instructional methods. Students are responsible for reviewing each section's instructional method and campus when registering.
Core Requirements
Course List | Code | Title | Hours |
| INSH 6300 | Research Methods in the Social Sciences | 4 |
| INSH 6500 | Statistical Analysis | 4 |
Required Concentration
Complete one of the following concentrations:
Electives
Course List | Code | Title | Hours |
| 12 |
| |
| |
| Human-Centered AI | |
| Exploring AI Trends and Tools | |
| AI Across the Health Sciences | |
| AI in Media Industries | |
| Responsible AI | |
Optional Co-op Experience
Course List | Code | Title | Hours |
| Co-op Work Experience
and Professional Development for Co-op | |
Program Credit/GPA Requirements
32 total semester hours required
Minimum 3.000 GPA required
Computational Social Science Concentration
Course List | Code | Title | Hours |
| DA 5030 | Introduction to Data Mining/Machine Learning | 4 |
| INSH 5301 | Introduction to Computational Statistics | 4 |
| INSH 5302 | Information Design and Visual Analytics | 4 |
| or PPUA 5262 | Big Data for Cities |
| or PPUA 5263 | Geographic Information Systems for Urban and Regional Policy |
Data Analytics in the Social Sciences Concentration
Course List | Code | Title | Hours |
| DA 5020 | Collecting, Storing, and Retrieving Data | 4 |
| or DA 5030 | Introduction to Data Mining/Machine Learning |
| INSH 5301 | Introduction to Computational Statistics | 4 |
| INSH 5302 | Information Design and Visual Analytics | 4 |
