This course will focus on advanced topics in survey design and analysis. Topics covered include different approaches to sampling, how to construct and use survey weights, and tools for analyzing and enriching survey data, including approaches to conducting matching and multiple imputation, as well as the construction and analysis of panel data. The course will also focus on designing and analyzing survey experiments.

This is a course on network analysis. The study of networks across the sciences has exploded recently. In this course, we will cover network scientific theory as it applies to the social sciences, network data collection and management, network visualization and description; and methods for the statistical analysis of networks. The course will make extensive use of real-world applications and students will gain a thorough background in the use of network analytic software.

The course is designed to prepare students for the job market through four units: (1) Identifying Your Talents; (2) Developing Your Professional Presence; (3) Polishing Your Professional Presence, and (4) Developing a Collaborative Mindset. Among other topics, there will be specific workshops with trained professionals and alumni on writing CVs and cover letters, interviewing, creating an elevator pitch, identifying and making the most of personal strengths (using the Clifton Strengths Assessment), building a personal website, and more.

Python has gained immense popularity as a programming language due to its ability to handle diverse types of data, powerful libraries for data analysis, robust support for tasks such as web scraping and data extraction from online sources, and its widespread use in machine learning and deep learning communities. Python is known for its readability and ease of use, making it a favorite among beginners and seasoned programmers alike.

This course is intended as a math "boot camp" for incoming DACSS students and PhD students in certain social and behavioral sciences. Students will develop or refresh math skills needed for effectively learning statistics and computational methods. Topics covered include essential algebra review (basic skills, functions, exponents and logarithms, trigonometric functions), differential calculus concepts (for optimization, as in estimation theory), integral calculus (for calculating probabilities), matrix algebra, vector spaces, eigenvalues (e.g.

How can social scientists convey data through narrative and reports geared toward general audiences or specific stakeholders? How can they convey those data through visuals geared toward non-scientists? This hands-on course provides students with the knowledge and skills needed to generate strong, data-driven communication.

This course serves as a rigorous introduction to quantitative empirical research methods, designed for doctoral students in social science and master?s students with a data analytics or computational social science focus. The material covered will include a brief introduction to the problem of causality, followed by modules on (1) measurement, (2) prediction, (3) exploratory data analysis (discovery), (4) probability (including distributions of random variables), and (5) uncertainty (including estimation theory, confidence intervals, hypothesis testing, power).

This course introduces students to the basic language of behavioral research, with an emphasis on designing valid social science research. An emphasis is placed on measurement reliability and validity, internal research design validity, and generalizability, or external research design validity.