Curriculum - MACSS


Real-World Analysis for Real-World Problems

In one year, you can earn a master’s degree that positions you for a lifetime of career opportunities. You will be among some of the first graduates with specialized training combining social-science ideas and findings with computational and statistical tools and methods to provide better solutions to real-world, human-based problems.

Curriculum Overview

The MaCSS curriculum integrates three components: computing tools and techniques, statistical approaches to data analysis, and social-science theories and findings. This structure will provide students with rigorous training in statistical and computational methods supported by a deep understanding of people, communities, and whole societies. The structure will also emphasize real-world applications of this knowledge.

Exploratory data analysis

Linear regression

Sensitivity analysis

Causal inference
Social Science:
Individual & group behavior

Interpersonal networks


Power & inequality
Machine learning

Cross validation

Natural language processing

Data visualization

Capstone Project

Students will work in small groups on a capstone project, analyzing data from one of our partner organizations. The capstone provides students with an opportunity to apply their coursework to a current real-world problem and hone their collaborative and leadership skills in preparation for their job search.

Summer Boot Camp – 6 units

MaCSS students need computing skills and proficiency in applied statistics to be successful in the program. Students who need additional training in applied statistics and computing can prepare for the program through our online summer boot camp. All students are required to take the boot camp courses; however, students can waive out of this requirement by passing MaCSS waiver exams in statistics and computing methods in May.

RStudio, univariate statistics, measurement reliability and validity, sampling and inference, hypothesis testing, linear regression.
Python using Jupyter notebooks, web-scraping & web-crawling, application programming interfaces (APIs)

Fall Semester – 14 units

Exploratory data analysis, multivariate & logistic regression, analysis of panel data, sensitivity/robustness checks, introduction to causal inference
Machine learning, test/training/validation data splits, version control, introduction to natural language processing, github
Potential harms in data collection & analysis, transparency, reproducibility & explainability in data analysis
The behavior of consumers and voters, social differences and inequalities, interpersonal networks
Careers in data analysis and social science, preparing for your job search, building your brand, interviewing skills, managing conflict

Spring Semester – 14 units

Causal inference: laboratory & natural experiments, differences-in-differences, regression discontinuity, instrumental variables, matching
Tables and charts, multivariate displays, networks & trees, geospatial maps, data aggregation, using color effectively
Analyze data to solve a real problem for a government, for-profit, or nonprofit organization
Regional variation in resources and culture, the behavior of firms & markets, power & politics
Navigating workplace cultures for career success