Free online courseData Analysis for Social Scientists

Course Description

Learn to turn real-world social science questions into evidence-based answers by building strong statistical intuition and practical data analysis habits. This course guides you from first principles to the kinds of decisions analysts face when working with surveys, experiments, and observational data, where the stakes are often public policy, health, education, and inequality. Rather than treating statistics as a set of formulas to memorize, you will develop a way of thinking that helps you ask better questions, spot weak conclusions, and communicate results with confidence.

You will start with the foundations of probability, random variables, and distributions, then move into the tools that make uncertainty manageable in practice. Along the way, you will learn how data is gathered, why sampling and measurement choices matter, and how to summarize and describe information without being misled by noise. You will build comfort with concepts like expectation and variance, and see how they connect to estimation, the behavior of sample averages, and why the central limit theorem is so useful when drawing conclusions from limited data.

As the course progresses, the focus shifts to statistical inference: how to construct estimators, judge their quality, and quantify uncertainty with confidence intervals, hypothesis tests, standard errors, and power calculations. You will learn to evaluate whether an apparent effect is likely to be real, how design decisions change what you can claim, and what can go wrong when results are overinterpreted. The course also introduces research ethics in human subjects work, reinforcing that responsible analysis involves both technical rigor and sound judgment.

A major theme is causality. You will learn why correlation is not enough, what assumptions sit behind causal claims, and how randomized experiments help identify effects. You will also tackle realistic complications, including noncompliance, interpretation challenges, and the kinds of incentive problems that can distort evidence. For observational studies, you will explore regression as a tool for explanation, along with key pitfalls such as omitted variable bias, endogeneity, and strategies like instrumental variables. These ideas provide a practical framework for understanding when a model supports a credible causal story and when it does not.

By the end, you will be better prepared to run and interpret linear and multivariate models, choose appropriate statistical tests, and present results clearly. You will also strengthen your ability to visualize data in ways that reveal patterns, support decisions, and avoid misleading impressions. If you want a grounded, job-relevant pathway into data science for social scientists, this course offers a rigorous, accessible bridge between theory and applied analysis.