Data Analysis for Social Scientists

Overview

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 [14.310x Data Analysis for Social Scientists](https://mitxonline.mit.edu/courses/course-v1:MITxT+14.310x/?utm_medium=ocw-website&utm_source=ocw-website&utm_campaign=dedp&utm_content=ocw-page-data-analysis-for-social-scientists).

Syllabus

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