Statistics and Data Science (Social Sciences Track) course Syllabus

Overview: This MicroMasters® program in Statistics and Data Science (Social Sciences Track) is designed for learners seeking to develop rigorous quantitative skills for policy analysis, social research, and evidence-based decision-making. The curriculum spans three core modules and a final proctored exam, with each module requiring 8–10 weeks of study at approximately 8–10 hours per week. The program emphasizes probability, regression modeling, causal inference, and machine learning applications using real-world social science data. Upon completion, learners earn the MITx MicroMasters® credential, ideal for careers in public policy, economics, and research or for advancement into graduate programs.

Module 1: Probability and Statistical Foundations

Estimated time: 80 hours

• Random variables and probability distributions
• Hypothesis testing and confidence intervals
• Sampling theory and statistical reasoning
• Applications to social data analysis

Module 2: Regression and Econometrics

Estimated time: 80 hours

• Linear and logistic regression models
• Causal inference and policy evaluation methods
• Model assumptions and diagnostic techniques
• Analysis of real-world social datasets using econometric tools

Module 3: Data Analysis and Machine Learning Applications

Estimated time: 80 hours

• Supervised learning methods for classification and prediction
• Model validation and performance metrics
• Machine learning techniques applied to economic and social data
• Interpretation of results for policy and research decisions

Module 4: Experimental Design and Observational Data Analysis

Estimated time: 40 hours

• Principles of experimental design in social sciences
• Analysis of observational data with confounding variables
• Techniques for reducing bias in non-experimental studies

Module 5: Advanced Econometrics and Causal Inference

Estimated time: 40 hours

• Instrumental variables and regression discontinuity designs
• Difference-in-differences and panel data methods
• Applications in policy impact evaluation

Module 6: Final Project

Estimated time: 40 hours

• Comprehensive proctored examination
• Application of statistical and causal inference methods to a policy-relevant research question
• Validation of mastery in data analysis and interpretation

Prerequisites

• Basic knowledge of calculus and linear algebra
• Comfort with mathematical reasoning and notation
• Familiarity with introductory statistics (helpful but not required)

What You'll Be Able to Do After

• Analyze social and economic data using regression and econometric models
• Design and evaluate policies using causal inference techniques
• Apply machine learning methods to real-world social science problems
• Interpret and communicate data-driven results for policy and research audiences
• Earn a credential that supports admission to graduate programs in economics, public policy, or data science