MIT 18.650 Statistics for Applications: study plan and how long it takes
The MIT OpenCourseWare playlist is 22 videos and 28.0 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.
22 videos · 28.0 hours · MIT OpenCourseWare
MIT 18.650 (Fall 2016), taught by Philippe Rigollet, is a mathematically rigorous statistics course: parametric inference, maximum likelihood estimation, the method of moments, hypothesis testing, goodness of fit, regression, Bayesian statistics, principal component analysis and generalized linear models.
It's popular with people moving into data science who want the theory behind the tools.
Who it's for: Learners comfortable with probability and calculus who want a rigorous statistics course.
How long does it take?
The full playlist is 28.0 hours across 22 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:
| Daily time | At 1x | At 1.5x | At 2x |
|---|---|---|---|
| 30 min a day | 57 days | 38 days | 29 days |
| 1 h a day | 29 days | 19 days | 15 days |
| 2 h a day | 15 days | 10 days | 8 days |
Make your own schedule
The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.
Week-by-week schedule
A sample plan at 1 hour a day and normal speed, using whole videos so none is split across days. That's 4 weeks in total. For a different pace, start date or speed, use the planner above.
Week 1Videos 1–7 · 9h 10m
Day 1 · 1h 18m
- 11. Introduction to Statistics1h 18m
Day 2 · 1h 17m
- 22. Introduction to Statistics (cont.)1h 17m
Day 3 · 1h 23m
- 33. Parametric Inference1h 23m
Day 4 · 1h 18m
- 44. Parametric Inference (cont.) and Maximum Likelihood Estimation1h 18m
Day 5 · 1h 17m
- 55. Maximum Likelihood Estimation (cont.)1h 17m
Day 6 · 1h 19m
- 66. Maximum Likelihood Estimation (cont.) and the Method of Moments1h 19m
Day 7 · 1h 19m
- 77. Parametric Hypothesis Testing1h 19m
Week 2Videos 8–14 · 9h 10m
Day 8 · 1h 19m
- 88. Parametric Hypothesis Testing (cont.)1h 19m
Day 9 · 1h 21m
- 99. Parametric Hypothesis Testing (cont.)1h 21m
Day 10 · 1h 23m
- 1011. Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit1h 23m
Day 11 · 1h 21m
- 1112. Testing Goodness of Fit (cont.)1h 21m
Day 12 · 1h 16m
- 1213. Regression1h 16m
Day 13 · 1h 14m
- 1314. Regression (cont.)1h 14m
Day 14 · 1h 15m
- 1415. Regression (cont.)1h 15m
Week 3Videos 15–21 · 8h 46m
Day 15 · 1h 18m
- 1517. Bayesian Statistics1h 18m
Day 16 · 1h 3m
- 1618. Bayesian Statistics (cont.)1h 3m
Day 17 · 1h 17m
- 1719. Principal Component Analysis1h 17m
Day 18 · 1h 17m
- 1820. Principal Component Analysis (cont.)1h 17m
Day 19 · 1h 15m
- 1921. Generalized Linear Models1h 15m
Day 20 · 1h 17m
- 2022. Generalized Linear Models (cont.)1h 17m
Day 21 · 1h 18m
- 2123. Generalized Linear Models (cont.)1h 18m
Week 4Video 22 · 55m
Day 22 · 55m
- 2224. Generalized Linear Models (cont.)55m
Tips for finishing
- Review probability first (Stanford CS109 or MIT 6.041) if the early lectures feel fast.
- Plan two lectures a week and do the problem sets on MIT OpenCourseWare.