Suggested preparation: Algebra and introductory statistics; basic programming is helpful.
Optional preparation: Data Analysis with Python: Free Course.
About this course
Learn to reason with data in social-science research. The lectures connect descriptive statistics and probability with regression, confidence intervals, hypothesis tests and statistical power.
Topics and lesson examples
- Lecture 03: Random Variables, Distributions, and Joint Distributions
- Lecture 06: Joint, Marginal, and Conditional Distributions
- Lecture 09: Expectation, Variance, and Introduction to Regression
- Lecture 11: Special Distributions, continued. The Sample Mean, Central Limit Theorem, and Estimation
- Lecture 16: (More) Explanatory Data Analysis: Nonparametric Comparisons and Regressions
Course at a glance
| Provider | MIT |
|---|---|
| Platform | YouTube · MIT OpenCourseWare |
| Access | Free Video |
| Level | Intermediate — WikiFree study-level assessment |
| Language | English |
| Available lessons | 23 |
| Video duration | 27h 54m · self-paced |
| Details checked | 10 October 2026 |
Preparation and study approach
Suggested preparation: Algebra and introductory statistics; basic programming is helpful.
Follow the available videos in sequence, pausing to summarize the main argument or reconstruct a worked example. Keep a short list of assumptions, unfamiliar terms and questions to revisit. Additional reading, problem solving and practice will take time beyond the listed video duration.
Using this video classroom
Select a lesson from the list, or search for a topic. Previous and Next move through the sequence. You can mark lessons complete; progress is stored in this browser without a WikiFree account.
Free access and course scope
The public MIT OpenCourseWare videos in this classroom are free to watch. This listing provides the available playlist recordings, which may include selected lectures, recitations, demonstrations or supporting discussions rather than every component of the original campus course. Textbooks, equipment, software licenses, supervised labs, graded assignments and instructor feedback are not supplied by WikiFree. No purchase is required to watch the videos or use the note-based study approach above.
WikiFree independently organizes these resources for self-paced study. Watching does not award an MIT certificate, academic credit or admission to MIT.
Video provider and credit: MIT OpenCourseWare on YouTube. WikiFree titles, descriptions and cover artwork are independently prepared.
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Intermediate
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — Free MIT Course: Data Analysis for Social Science | Intermediate | Lecture 03: Random Variables, Distributions, and Joint Distributions · Lecture 06: Joint, Marginal, and Conditional Distributions · Lecture 09: Expectation, Variance, and Introduction to Regression | 27h 54m of video; practice time is additional |
| Data Analysis with Python: Free Course | Beginner | Python · NumPy · pandas | 12h 6m of video; practice time is additional |
| Free Advanced Data Analytics Course: Statistics and ML | Intermediate | Data science workflow · Data interpretation · Statistics | 21h 32m of video; practice time is additional |
| Excel Data Analysis: Free Course | Intermediate | Excel data analysis · Table organisation · Conditional calculations | 7h 13m of video; practice time is additional |
| Free MIT Course: Impact Evaluation and Randomized Policy Experiments | Intermediate | MIT Course: Impact Evaluation and Randomized Policy Experiments | 12h 12m of video; practice time is additional |
Provider: MIT. Videos play from the MIT OpenCourseWare channel on YouTube.
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