MITx: Fundamentals of Statistics course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

Overview: This course offers a rigorous, mathematically grounded introduction to statistics, designed to build strong analytical and data-driven decision-making skills. Organized into five core modules, it covers probability, statistical inference, regression, and advanced modeling concepts. With a total time commitment of approximately 12–20 weeks at 6–10 hours per week, learners engage with theoretical foundations and practical applications essential for data science and quantitative fields.

Module 1: Probability Foundations

Estimated time: 30 hours

Random variables and sample spaces
Discrete and continuous probability distributions
Expected value, variance, and moments
Binomial, geometric, and normal distributions

Module 2: Statistical Inference

Estimated time: 40 hours

Sampling distributions and the Central Limit Theorem
Confidence intervals for means and proportions
Hypothesis testing and p-values
Interpretation of statistical significance

Module 3: Regression and Data Modeling

Estimated time: 40 hours

Simple linear regression and least squares
Correlation and model fit
Residual analysis and assumptions
Interpretation of regression coefficients

Module 4: Advanced Statistical Concepts

Estimated time: 30 hours

Maximum likelihood estimation
Bias, variance, and trade-offs
Model evaluation and selection

Module 5: Applications in Data-Driven Decision Making

Estimated time: 20 hours

Statistical reasoning in research
Decision-making under uncertainty
Case studies in engineering, science, and business

Module 6: Final Project