Data Analysis for Social Scientists

Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest.

start date
February 1, 2022
length
Estimated 11 Weeks
effort
12–14 hours per week

About this course

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP), which provides a path towards MIT’s Master’s in DEDP. To enroll in the courses, remain on this site and click the “enroll now” button. If you want to earn a certificate for the courses or start your path towards a MicroMasters program credential, please visit the MicroMasters portal after you enroll.

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.

Course Previews:

Our course previews are meant to give prospective learners the opportunity to get a taste of the content and exercises that will be covered in each course. If you are new to these subjects, or eager to refresh your memory, each course preview also includes some available resources. These resources may also be useful to refer to over the course of the semester.

A score of 60% or above in the course previews indicates that you are ready to take the course, while a score below 60% indicates that you should further review the concepts covered before beginning the course.

Please use this link to access the course preview.

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

Prerequisites

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

Meet your instructors

  • Esther Duflo

    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 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.