Statistics and Data Science (General Track) course Syllabus

This MicroMasters® program provides a rigorous, graduate-level education in statistics, data science, and machine learning, designed for learners with strong mathematical and programming backgrounds. The curriculum spans approximately 32–40 weeks of intensive study, with each module requiring 8–10 weeks and 10–12 hours per week. Learners will develop deep expertise in probability, statistical inference, regression, machine learning, and large-scale data analysis, culminating in a proctored capstone exam. Lifetime access allows flexible, self-paced learning.

Module 1: Probability and Statistics Foundations

Estimated time: 80 hours

• Random variables and probability distributions
• Expectation, variance, and moments
• Sampling theory and central limit theorem
• Hypothesis testing and confidence intervals
• Statistical reasoning and inference

Module 2: Data Analysis and Regression

Estimated time: 80 hours

• Linear regression models and assumptions
• Logistic regression for classification
• Model diagnostics and residual analysis
• Interpretation of regression results
• Application to real-world datasets using Python

Module 3: Machine Learning

Estimated time: 80 hours

• Supervised learning: classification and regression
• Unsupervised learning: clustering and dimensionality reduction
• Bias-variance trade-off and model complexity
• Overfitting and regularization techniques
• Implementation of ML algorithms in Python

Module 4: Advanced Data Science

Estimated time: 80 hours

• Large-scale data analysis and processing
• Computational modeling and optimization
• End-to-end data science workflows
• Statistical reasoning in real-world problems

Module 5: Capstone Exam Preparation

Estimated time: 40 hours

• Review of core statistical concepts
• Integration of machine learning and data analysis
• Practice problems and mock assessments

Module 6: Final Project

Estimated time: 60 hours

• Comprehensive proctored capstone exam
• Application of statistical and ML methods to real data
• Submission and validation for MicroMasters® credential

Prerequisites

• Strong background in calculus and linear algebra
• Familiarity with probability theory
• Programming experience, preferably in Python

What You'll Be Able to Do After

• Apply rigorous statistical methods to real-world data
• Build and evaluate regression and machine learning models
• Perform large-scale data analysis using Python
• Solve complex data science problems with computational tools
• Earn a credential that supports admission to advanced degree programs