subject
Intro

edX: Data Analysis for Social Scientists

 with  Esther Duflo and Sara Fisher Ellison

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy. To enroll in the full program, go to MIT’s MicroMasters site. If you want to enroll in this course only, click “Enroll Now.” Learn more about this program and how it integrates with MIT’s new blended master’s degree.

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.

Syllabus

MODULE 0: THE BASICS OF R
  • Introduction to the software R with suggested resources.
MODULE 1: INTRODUCTION
  • Introduction to the power of data and data analysis, and course overview
MODULE 2: FUNDAMENTALS OF PROBABILITY, RANDOM VARIABLES, DISTRIBUTIONS AND JOINT DISTRIBUTIONS
  • Basics of probability and introduction to random variables
  • Distributions and joint distributions
MODULE 3: GATHERING AND COLLECTING DATA, ETHICS, AND KERNEL DENSITY ESTIMATES
  • Collecting data through surveys, web scraping, and other data collection methods
  • Principles and practical steps for protection of human subjects in research
  • Discussion of kernel density estimates
MODULE 4: JOINT, MARGINAL, AND CONDITIONAL DISTRIBUTIONS & FUNCTIONS OF RANDOM VARIABLES
  • Further exploration on joint, marginal, and conditional distributions
  • Deep dive intofunctions of random variables
MODULE 5: MOMENTS OF A RANDOM VARIABLE, APPLICATIONS TO AUCTIONS, & INTRO TO REGRESSION
  • Moments of a distribution, expectation, and variance
  • Applying principles of probability to the analysis of auctions
  • Basics of regression analysis
MODULE 6: SPECIAL DISTRIBUTIONS, THE SAMPLE MEAN, CENTRAL LIMIT THEOREM, AND ESTIMATION
  • The properties of special distributions with several examples
  • Statistics: Introduction to the sample mean, central limit theorem, and estimation
MODULE 7: ASSESSING AND DERIVING ESTIMATORS- CONFIDENCE INTERVALS AND HYPOTHESIS TESTING
  • Deriving and assessing estimators
  • Constructing and interpreting confidence intervals
  • Introduction to hypothesis testing
MODULE 8: CAUSALITY, ANALYSING RANDOMIZED EXPERIMENTS, & NONPARAMETRIC REGRESSION
  • Understanding randomization in the context of experimentation
  • Introduction to nonparametric regression techniques
MODULE 9: SINGLE AND MULTIVARIATE LINEAR MODELS
  • In-depth discussion of the linear model and the multivariate linear model
MODULE 10: PRACTICAL ISSUES IN RUNNING REGRESSIONS, AND OMITTED VARIABLE BIAS
  • Covariates, fixed effects, and other functional forms
  • Introduction to regression discontinuity design
MODULE 11: INTRO TO MACHINE LEARNING AND DATA VISUALIZATION
  • Use of machine learning for prediction, covers tuning and training
  • Principles of data visualization
MODULE 12: ENDOGENEITY, INSTRUMENTAL VARIABLES, AND EXPERIMENTAL DESIGN
  • Understanding endogeneity problems  and an introduction to instrumental variables and two stage least squares, and assessing the validity of an instrument
  • Designing an effective experiment with a case study from Indonesia
3 Student
reviews
Cost Free Online Course
Subject Data Analysis
Provider edX
Language English
Hours 12 hours a week
Calendar 12 weeks long

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Reviews for edX's Data Analysis for Social Scientists
4.3 Based on 3 reviews

  • 5 stars 67%
  • 4 star 0%
  • 3 star 33%
  • 2 star 0%
  • 1 star 0%

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  • 1
5.0 3 months ago
by Paul F. Groepler Sr. is taking this course right now, spending 16 hours a week on it and found the course difficulty to be hard.
To say this class is thorough is an understatement. The lectures are extremely detailed, sometimes with additional detailed references(!), and it occasionally warrants going back and replaying one or two of the lectures before moving on. There is a good deal of statistics and probability review and training prior to getting to the "methods" of this class (around week 8). I recommend this course as I cannot imagine a better, more thorough treatment for the topic, taught by some of the "best" there are out there today in Economics and Statistics.
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3.0 10 months ago
Mariana Marcondes dropped this course.
I was very excited about this course - its scope and the fact that it did not require any knowledge in statistics. That is not true: you should know some probability and statistics, otherwise you will not be able to keep up with the workload (or the classes, to be honest) and will drop out - like I did.

Will try again later, when I have gained some statistics knowledge.
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5.0 a year ago
by Harunpehlivan audited this course.
0 person found
this review helpful
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