Data Analysis for Social Scientists
Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest.
Course Information
Certificate Track
Learn for Free
About this Course
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.
This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively.
The course is free to audit. Learners can take a proctored exam and earn a course certificate by paying a fee, which varies by ability to pay. Please see our FAQ articles for more information on the certificate and audit track features as well as more information on the pricing structure. Enroll in this course by selecting the "enroll now" button at the top of the page.
This course can be completed by itself or as part of the MITx MicroMasters program in Data, Economics, and Design of Policy (DEDP), which provides a path toward the master’s in DEDP at MIT.
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.
How you'll learn
Practical ApplicationApply your new knowledge with hands-on, practical exercises drawn from healthcare, sports, finance, sustainability, and more.
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
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.
