Data Analysis for Social Scientists

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Course
en
English
132 h
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  • 11 Sequences
  • Advanced Level
  • Starts on October 4, 2021
  • Ends on December 20, 2021

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Course details

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: Endogeneity, Instrumental Variables, and Experimental Design
Week Eleven: Intro to Machine Learning and Data Visualization
Optional: Writing an Empirical Paper

Prerequisite

No prior preparation in probability and statistics is required, but familiarity with algebra and calculus is assumed.

Instructors

Esther Duflo
Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics, winner of the 2019 Nobel Prize in Economic Sciences
MIT

Sara Fisher Ellison
Senior Lecturer, Economics
MIT

Editor

MIT is a world-class educational institution where teaching and research — with relevance to the practical world as a guiding principle — continue to be its primary purpose.

MIT is independent, coeducational, and privately endowed. Its five schools and one college encompass numerous academic departments, divisions and degree-granting programs, as well as interdisciplinary centers, laboratories and programs whose work cuts across traditional departmental boundaries.

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