This is the 2nd semester of a two-semester course that provides a rigorous introduction to methods and tools in advanced data analytics for social science doctoral students. The goal of the course is to provide students with a strong foundation of knowledge of core methods, thereby preparing them to contribute to research teams, to conduct their own research, and to enroll in more advanced courses. The course will cover research reproducibility (fall), machine learning (fall), natural language processing (spring), and causal inference (spring). In contrast to other courses currently offered on campus, this course’s intended audience is applied researchers, typically social science doctoral students in their 2nd or 3rd yr of graduate school.

This is the second semester of a two-semester course that provides a rigorous introduction to methods and tools in advanced data analytics for social science doctoral students. The goal of the course is to provide students with a strong foundation of knowledge of core methods, thereby preparing them to contribute to research teams, to conduct their own research, and to enroll in more advanced courses. The course will cover research reproducibility (fall), machine learning (fall), natural language processing (spring), and causal inference (spring). In contrast to other courses currently offered on campus, this course’s intended audience is applied researchers, typically social science doctoral students in their second or third year of graduate school. This is a required course for students in the Computational Social Science Training Program. Enrollment is open to doctoral students from any department. Students who have not taken SOCIOL 273L should consult the instructor before enrolling. The course is divided into modules, each lasting 3-5 weeks. Each module will include lectures, discussion of example research articles, lab exercises, and a group project involving Python or R programming. Projects, typically done in groups of 3 students, will also provide the opportunity to practice reproducibility techniques, data manipulation and transformation, and data science workflows.

Repeat Rules

Course is not repeatable for credit.