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.

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.

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.

The theory seminar is a weekly meeting in which topics of interest in the theory of computation - broadly construed - are presented. This is sometimes new research by visitors or local people. It is sometimes work in progress, and it is sometimes recent material of others that some of us present in order to learn and share. This is a one-credit seminar which may be taken repeatedly for credit. May be repeated for credit up to six times.

Teaching Assistants as Tomorrow's Faculty prepares Teaching Assistants (TAs) at the College of Information and Computer Sciences to fulfill their duties in an effective and pedagogically sound manner. The two credit (not repeatable) course is semester long and taken by all TAs prior to assuming assistantship.

Teaching Assistants as Tomorrow's Faculty prepares Teaching Assistants (TAs) at the College of Information and Computer Sciences to fulfill their duties in an effective and pedagogically sound manner. The two credit (not repeatable) course is semester long and taken by all TAs prior to assuming assistantship.