Data Literacy

This course introduces students to data and statistics, providing a framework for thinking about the various purposes of statistical analysis. Participants will learn how to interpret descriptive statistics, causal analyses, and visualizations to draw meaningful insights. The course covers the development of a research study for causal analysis, computation and interpretation of descriptive statistics, and the design of effective visualizations. It aims to equip individuals with the fundamental tools of data literacy for data-driven decision-making.

The Measurement course provides a framework for creating and evaluating quantitative measures. Participants will explore various approaches for quantifying complex concepts such as health, educational attainment, and trust in government. The course covers different levels of measurement, ways to transform variables, construction and evaluation of measurement models, surveys, and the judgment of measure quality. This course aims to equip individuals with the ability to develop and critically assess measures for concepts worth studying, essential for a good analysis.

This course introduces the linear regression model, a powerful tool for measuring the relationship between multiple variables. Participants will learn to interpret and critically evaluate a multivariate regression analysis, including the components of a bivariate regression model, creating and interpreting multivariate, binary dependent variable, and interactive models, and incorporating different types of variables into a model. The course aims to provide a comprehensive understanding of regression models for both descriptive and causal inference.

The Probability and Uncertainty course focuses on how analysts can measure and describe the confidence they have in their findings. Participants will learn about key probability rules and concepts, application to variables and their associated probability distributions, computation and interpretation of uncertainty, and the role of hypothesis testing in a regression context. By the end of the course, individuals should be able to discuss statistical findings in probabilistic terms and interpret the uncertainty of a particular estimate.

In the Data Literacy Capstone course, participants apply the skills and knowledge acquired in the specialization to critically evaluate an original quantitative analysis. The project involves identifying and reading a piece of high-quality, original, quantitative research, interpreting and evaluating the findings and methodological approach, and reviewing other students' submissions. The course aims to empower individuals to be critical consumers and users of quantitative research, essential for data-driven decision-making.