Statistics and Data Science (Methods Track) course Syllabus

This course is part of the MITx MicroMasters® Methods Track in Statistics and Data Science, offering rigorous, graduate-level training in the mathematical and methodological foundations of the field. The program is structured into five core modules followed by a comprehensive capstone exam, with each module requiring approximately 8–10 weeks of effort at 10–12 hours per week. Learners will gain deep theoretical understanding of probability, statistical inference, regression modeling, machine learning theory, and advanced statistical methods. The curriculum is highly quantitative and designed for those aiming to pursue research, PhD studies, or advanced roles in data science and AI. Successful completion culminates in a proctored final examination and eligibility for the MITx MicroMasters® credential.

Module 1: Probability Theory and Statistical Foundations

Estimated time: 90 hours

• Random variables and probability distributions
• Expectation, variance, and moments
• Limit theorems (Law of Large Numbers, Central Limit Theorem)
• Sampling distributions and foundational inference frameworks

Module 2: Regression and Statistical Modeling

Estimated time: 90 hours

• Linear and generalized linear models
• Maximum likelihood estimation
• Model diagnostics and assumption checking
• Application of regression techniques to complex datasets

Module 3: Machine Learning Theory

Estimated time: 90 hours

• Theoretical foundations of supervised and unsupervised learning
• Bias-variance trade-off and model complexity
• Optimization algorithms in machine learning
• Statistical evaluation of predictive models

Module 4: Advanced Statistical Methods

Estimated time: 90 hours

• High-dimensional data analysis
• Advanced statistical estimation techniques
• Model selection and regularization methods

Module 5: Capstone Exam Preparation

Estimated time: 60 hours

• Comprehensive review of probability and inference
• Integration of regression and machine learning theory
• Practice with rigorous problem-solving and theoretical proofs

Module 6: Final Project

Estimated time: 40 hours

• Deliverable 1: Real-world data analysis using statistical modeling
• Deliverable 2: Theoretical justification of methodological choices
• Deliverable 3: Comprehensive report and model evaluation

Prerequisites

• Strong background in calculus and linear algebra
• Working knowledge of probability theory
• Proficiency in programming (Python or R) and mathematical reasoning

What You'll Be Able to Do After

• Apply advanced statistical inference to real-world data
• Develop and evaluate regression and machine learning models with theoretical rigor
• Conduct high-dimensional data analysis using modern techniques
• Prepare for PhD-level research in statistics or data science
• Earn a recognized credential for roles in quantitative research, AI, and data science