Week One: Introduction
- Introduction to the software R with exercises. Suggested resources for learning more on the web.
- Introduction to the power of data and data analysis, overview of what will be covered in the course.
Week Two: Fundamentals of Probability, Random Variables, Joint Distributions, and Collecting Data
- Basics of probability and introduction to random variables
- Discussion of distributions and joint distributions
- Introduction to collecting data through surveys, web scraping, and other data collection methods
Week Three: Describing Data, Joint, and Conditional Distributions of Random Variables
- Principles and practical steps for protection of human subjects in research
- Discussion of kernel density estimates
- Builds on basics from module 2 to cover joint, marginal, and conditional distributions
Week Four: Joint, Marginal, and Conditional Distributions and Functions of Random Variables
- Similarly builds on the basics from week 2 to cover functions of random variables
- Discussion of moments of a distribution, expectation, and variance
- Basics of regression analysis
- Application: Application of some principles of probability to the analysis of auctions
Week Five: Special Distributions, The Sample Mean, The Central Limit Theorem, and Estimation
- Discussion of properties of special distribution with several examples
- Statistics: Introduction to the sample mean, central limit theorem, and estimation
Week Six: Assessing and Deriving Estimators, Confidence Intervals
- Deriving and assessing estimators
- Constructing and interpreting confidence intervals
- Introduction to hypothesis testing
Week Seven: Causality, Analyzing Randomized Experiments, and Nonparametric Regression
- Understanding randomization in the context of experimentation
- Introduction to nonparametric regression techniques
Week Eight: Single and Multivariate Linear Models
- In-depth discussion of the linear model and the multivariate linear model
Week Nine: Practical Issues in Running Regressions and Omitted Variable Bias
- Covariates, fixed effects, and other functional forms
- Introduction to regression discontinuity design
Week Ten: Endogeneity, Instrumental Variables, and Experimental Design
- Understanding the problem of endogeneity; introduction to instrumental variables and two-stage least squares, with a discussion of how to assess the validity of an instrument
- Discussion of how to design the effective experiment, followed by an example from Indonesia
- Principles of data visualization with examples of well-crafted visual presentations of data
Week Eleven: Intro to Machine Learning and Data Visualizations
- Introduction to the use of machine learning for prediction. Covers tuning and training. [Note: These lectures were given by a guest lecturer and are not available to OCW users.]
