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.

Algorithmic systems are at the center of today's digital world, and mediate communication processes in areas as diverse as social media, journalism, healthcare, and governments. How do algorithmic systems capture, represent, and transmit information about everyday interactions? How do they shape, and are shaped by, social, cultural, and political life? What kind of new issues and concerns arise from their ubiquitous use? This course provides a critical introduction to algorithmic systems, and how they relate to issues of communication, power and inequalities in society.

What is public opinion? How do we measure it? Where does it come from? Does it?and should it?matter for policies and political outcomes? Whose opinions count? These are fundamental questions for understanding the role of citizens in American democracy, and democracy in general. In this course, we learn about how members of the public think about issues, and why they think the way they do. We will also examine whether or not political leaders follow the "the will of the public" or manipulate public opinion to achieve their own aims.

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.

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.

This course provides students with an introduction to the R programming language that will be used in all core courses and many of the technical electives. There is a growing demand for students with a background in generalist data science languages such as R, as opposed to more limited software such as Excel or statistics packages such as SPSS or Stata. The course will also provide students with a solid grounding in general data management and data wrangling skills that are required in all advanced quantitative and data analysis courses.