MIT 6.0002 Computational Thinking and Data Science: study plan and how long it takes
The MIT OpenCourseWare playlist is 15 videos and 12.2 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.
15 videos · 12.2 hours · MIT OpenCourseWare
MIT 6.0002 (Fall 2016), the follow-on to 6.0001, uses Python to teach computational thinking for data: optimization problems, graph models, stochastic thinking, random walks, Monte Carlo simulation, confidence intervals, sampling, experimental data, and an introduction to machine learning with clustering and classification.
It ends with a memorable lecture on statistical sins.
Who it's for: Learners who finished an intro Python course (like MIT 6.0001) and want to apply programming to data and statistics.
How long does it take?
The full playlist is 12.2 hours across 15 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 | 25 days | 17 days | 13 days |
| 1 h a day | 13 days | 9 days | 7 days |
| 2 h a day | 7 days | 5 days | 4 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 3 weeks in total. For a different pace, start date or speed, use the planner above.
Week 1Videos 1–7 · 5h 39m
Day 1 · 41m
- 11. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)41m
Day 2 · 48m
- 22. Optimization Problems48m
Day 3 · 50m
- 33. Graph-theoretic Models50m
Day 4 · 50m
- 44. Stochastic Thinking50m
Day 5 · 49m
- 55. Random Walks49m
Day 6 · 50m
- 66. Monte Carlo Simulation50m
Day 7 · 50m
- 77. Confidence Intervals50m
Week 2Videos 8–14 · 5h 46m
Day 8 · 47m
- 88. Sampling and Standard Error47m
Day 9 · 47m
- 99. Understanding Experimental Data47m
Day 10 · 51m
- 1010. Understanding Experimental Data (cont.)51m
Day 11 · 52m
- 1111. Introduction to Machine Learning52m
Day 12 · 51m
- 1212. Clustering51m
Day 13 · 50m
- 1313. Classification50m
Day 14 · 49m
- 1414. Classification and Statistical Sins49m
Week 3Video 15 · 45m
Day 15 · 45m
- 1515. Statistical Sins and Wrap Up45m
Tips for finishing
- Do the problem sets on MIT OpenCourseWare; they're where simulation and modelling click.
- Watch it before a machine learning course; the last few lectures are a gentle introduction.