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Data Analysis for Social Scientists

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Course Highlights
  • MIT via edX
  • 11 week (12-14 hours weekly) of effort required
  • Language: English
  • Course level: Advanced

This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, 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 for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses. This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively.

What you’ll learn

  • Intuition behind probability and statistical analysis
  • How to summarize and describe data
  • A basic understanding of various methods of evaluating social programs
  • How to present results in a compelling and truthful way
  • Skills and tools for using R for data analysis

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Syllabus

14.310x – Data Analysis for Social Scientists

Week One: Introduction
Week Two: Fundamentals of Probability, Random Variables, Joint Distributions and Collecting Data
Week Three: Describing Data, Joint and Conditional Distributions of Random Variables
Week Four: Functions and Moments of a Random Variables & Intro to Regressions
Week Five: Special Distributions, the Sample Mean, the Central Limit Theorem
Week Six: Assessing and Deriving Estimators – Confidence Intervals, and Hypothesis Testing
Week Seven: Causality, Analyzing Randomized Experiments, & Nonparametric Regression
Week Eight: Single and Multivariate Linear Models
Week Nine: Practical Issues in Running Regressions, and Omitted Variable Bias
Week Ten: Intro to Machine Learning and Data Visualization
Week Eleven: Endogeneity, Instrumental Variables, and Experimental Design
Optional: Writing an Empirical Paper

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