Data Analysis for Social Scientists

  • Free to audit
  • Paid certificate
  • Course certificate
  • 14 weeks
Provider
MIT
Cost
Free to audit
Certificate
Paid
Duration
14 weeks
Effort
12–14 hrs/week
Format
Instructor-paced
Language
English
Subjects
Data Science
Source
MIT Open Learning
Last verified
14 Sep 2026

All material is free; graded work and the certificate are paid.

14.310x Data Analysis for Social Scientists is an MITx Online course in the MicroMasters Program in Data, Economics, and Design of Policy (DEDP). It introduces the essential ideas of probability and statistics and teaches techniques of modern data analysis with applications drawn from real-world examples and frontier research, including instruction in the statistical package R with opportunities for self-directed empirical work. The topic list moves from data analysis in R and the fundamentals of probability, random variables and joint distributions, through collecting and describing data, special distributions, the sample mean, the central limit theorem and estimation, to assessing estimators, confidence intervals and hypothesis testing. It then covers causality and randomised experiments, nonparametric regression, single and multivariate linear models, practical regression issues and omitted variable bias, endogeneity, instrumental variables and experimental design, and finishes with machine learning and data visualisation.

The course is instructor-paced over 14 weeks at an estimated 12 to 14 hours per week and is designed for anyone who wants to work with data and communicate findings. Auditing is free with full access to the materials. The certificate requires a proctored exam and a fee that varies by ability to pay; the page lists a certificate track of $250 to $1,000 with financial aid, and completion can count toward the DEDP MicroMasters.

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What you’ll learn

  • Analyse data in R
  • Apply the fundamentals of probability, random variables and joint and conditional distributions
  • Use the sample mean, the central limit theorem and estimation
  • Build confidence intervals and run hypothesis tests
  • Analyse randomised experiments and reason about causality
  • Fit single and multivariate linear models and handle omitted variable bias, endogeneity and instrumental variables

Who it’s for

Social scientists, policy analysts and anyone who wants a statistics course taught through R and real research examples, with the option of MicroMasters credit.

Source: MIT Open Learning (opens in a new tab) · Verified · Report a change

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