Data Analysis for Social Scientists
Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest.
Data Analysis for Social Scientists
Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest.
In this course, we will introduce you to the essential notions of probability and statistics. You will learn techniques in modern data analysis with applications drawn from real world examples and frontier research. You will also receive instruction for how to use the statistical package R with opportunities to perform self-directed empirical analyses.
What you'll learn
The course will investigate the following topics:
- Data analysis in R
- Fundamentals of probability, random variables, and joint distributions
- Collecting and describing data
- Joint and conditional distributions of random variables
- Joint, marginal, and conditional distributions, and functions of random variables
- Special distributions, the sample mean, the central limit theorem, and estimation
- Assessing and deriving estimators, confidence intervals, and hypothesis testing
- Causality, analyzing randomized experiments, and nonparametric regression
- Single and multivariate linear models
- Practical issues in running regressions and omitted variable bias
- Endogeneity, instrumental variables, and experimental design
- Machine learning and data visualization
Access the full syllabus here.
Prerequisites
No prior preparation in probability and statistics is required, but familiarity with algebra and calculus is assumed.
Course Readiness Check:
Our course readiness checks help you determine if you should review key concepts before starting the course.
Please use this link to access the course readiness check and answer key.
Meet your instructors
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Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics -
Senior Lecturer
Esther Duflo
Esther Duflo
Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics
Esther Duflo is the winner of the 2019 Nobel Prize in Economic Sciences. She is also the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics at MIT. She was educated at the Ecole Normale Supérieure, in Paris, and at MIT. She has received numerous honors and prizes including a John Bates Clark Medal for the best American economist under 40 in 2010, a MacArthur “Genius” Fellowship in 2009. She was recognized as one of the best eight young economists by The Economist magazine, one of the 100 most influential thinkers by Foreign Policy since the list exists, and one of the “Forty under 40” most influential business leaders under forty by Fortune magazine in 2010.
To learn more, please click here.
Sara Fisher Ellison
Sara Fisher Ellison
Senior Lecturer
Sara Fisher Ellison is a Senior Lecturer in the MIT Economics Department. She was an undergraduate at Purdue University and received graduate degrees from both Cambridge University and MIT. She has been a fellow at both the Institute for Advanced Study and the Hoover Institute. Her recent research has investigated a number of questions in industrial organization, with a focus on the pharmaceutical industry and ecommerce. She serves on a number of editorial boards. She has taught at the undergraduate, MBA, and Ph.D. levels, and has received awards for both outstanding teaching and pedagogical innovation. To learn more, please click here.