Catalog

Data Analysis for Social Scientists

Economics MIT CC BY-NC-SA 4.0 23 lectures

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses. **MITx Online** This course draws materials from *14.310x Data Analysis for Social Scientists*, which is part of the MicroMasters Program in Data, Economics, and Design of Policy offered by MITx Online. The MITx Online course is entirely free to audit, though learners have the option to pay a fee, which is based on the learner’s ability to pay, to take the proctored exam and earn a course certificate. To access that course, create an MITx Online account and enroll in the course {{% resource_link "b53a3134-e707-4662-aa42-a67207bd598f" "14.310x Data Analysis for Social Scientists" %}}.

Syllabus

  1. 1 Lecture 01: Introduction to 14.310x Data Analysis for Social Scientists
  2. 2 Lecture 02: Fundamentals of Probability
  3. 3 Lecture 03: Random Variables, Distributions, and Joint Distributions
  4. 4 Lecture 04: Gathering and Collecting Data
  5. 5 Lecture 05: Summarizing and Describing Data
  6. 6 Lecture 06: Joint, Marginal, and Conditional Distributions
  7. 7 Lecture 07: Functions of Random Variables
  8. 8 Lecture 08: Moments of Distribution
  9. 9 Lecture 09: Expectation, Variance, and Introduction to Regression
  10. 10 Lecture 10: Special Distributions
  11. 11 Lecture 11: Special Distributions, continued. The Sample Mean, Central Limit Theorem, and Estimation
  12. 12 Lecture 12: Assessing and Deriving Estimators
  13. 13 Lecture 13. Confidence Intervals, Hypothesis Testing, and Power Calculations
  14. 14 Lecture 14: Causality
  15. 15 Lecture 15: Analyzing Randomized Experiments
  16. 16 Lecture 16: (More) Explanatory Data Analysis: Nonparametric Comparisons and Regressions
  17. 17 Lecture 17: The Linear Model
  18. 18 Lecture 18: The Multivariate Model
  19. 19 Lecture 19: Practical Issues in Running Regressions
  20. 20 Lecture 20: Omitted Variable Bias
  21. 21 Lecture 21: Endogeneity and Instrument Variables
  22. 22 Lecture 22: Experimental Design
  23. 23 Lecture 23: Visualizing Data

Course materials