Statistics for Applications (18.650)
About this course
MIT 18.650 is a graduate-level statistics course covering the mathematical foundations of statistical inference: parameter estimation and maximum likelihood, confidence intervals, hypothesis testing (frequentist and Bayesian), linear and logistic regression, and goodness-of-fit tests. Philippe Rigollet teaches it with full mathematical rigor — proofs, not just formulas — at the pace MIT graduate students experience.
This is the statistics course for learners who found applied statistics courses like Khan Academy's or Coursera's data analytics statistics sections too shallow, and want the mathematical foundations that ML and data science theory build on. It complements MIT 18.06 Linear Algebra as the statistics counterpart in a rigorous mathematical foundation for data science.
What you'll learn
This course includes
Compare alternatives for Statistics for Applications (18.650)
- Price
- FreeCompletely free, openly licensed — no certificate
- Duration
- 30 hrs
- Level
- Intermediate
- Certificate
- Price
- FreeCompletely free, openly licensed — no certificate
- Duration
- 34 hrs
- Level
- Intermediate
- Certificate
- Price
- FreeFree lecture materials; some versions paid
- Duration
- 50 hrs
- Level
- Advanced
- Certificate
- Price
- FreeFree lecture materials; some versions paid
- Duration
- 50 hrs
- Level
- Advanced
- Certificate
Instructor
Taught by Philippe Rigollet, Professor of Mathematics at MIT, whose research focuses on statistics and machine learning theory.
Requirements
- Calculus and linear algebra comfort; probability basics
Who this course is for
- Data scientists who want rigorous mathematical statistics beyond applied tutorials
- ML practitioners who want the statistical theory underlying their methods
- Graduate students in data science or related fields supplementing coursework