Data Analytics 3100: Introduction to Statistical Methods exposes students to the process by which quantitative social science and data science research is conducted. The class revolves around three separate, but related tracks. Track one teaches some basic tools necessary to conduct quantitative social science research. Topics covered include descriptive statistics, sampling, probability, and statistical theory. Track two teaches students how to implement these basic tools using R. The third track teaches students the fundamentals of research design. Topics will include independent and dependent variables, generating testable hypotheses, and issues in causality.
Certificate students and individual course takers must complete DATA 1010: Introduction to Data Analytics and Data Analytics 2100: Intermediate Data Analytics before enrolling in this course. Students who complete all four courses in order earn the Certificate in Data Analytics.
*Academic credit is defined by the University of Pennsylvania as a course unit (c.u.). A course unit (c.u.) is a general measure of academic work over a period of time, typically a term (semester or summer). A c.u. (or a fraction of a c.u.) represents different types of academic work across different types of academic programs and is the basic unit of progress toward a degree. One c.u. is usually converted to a four-semester-hour course.
Instructor
Samantha Sangenito
- Data Scientist, Penn Program on Opinion Research and Election Studies
Samantha Sangenito is a data scientist at the Penn Program on Opinion Research and Election Studies. She teaches courses in data analytics at the undergraduate level through PORES and at the graduate level through the Fels Institute of Government. As the Associate Director of Programs in Data Analytics at… Read more
