DATA 310: Introduction to Statistical Methods

Data Analytics
Course in Data Analytics
Course Description:

Data Analytics 310: 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 101: Introduction to Data Analytics and Data Analytics 210: Intermediate Data Analytics before enrolling in this course. Although courses in the Certificate in Data Analytics must be taken sequentially to build your expertise in data analytics, you have the option to enroll in one or more courses in order without committing to the entire certificate. Students who complete all four courses in order earn the Certificate in Data Analytics.

Course Credits:
1 course unit (c.u.)*
Term Format:
Accelerated 8-Week Term
2021 Term Offered:
Spring 1 (accelerated): Jan 13 – Mar 10, 2021
Fall 1 (accelerated): Aug 31 – Oct 25, 2021
2022 Term Offered:
Spring 1 (accelerated): Jan 12 – Mar 9, 2022
Prerequisites
DATA 101, DATA 210
Synchronous Session:
Weekly synchronous session required
Fall 1 online seminar meets Thursdays from 6 - 7 p.m., EST
Spring 1 online seminar meets Thursdays from 6 - 7 p.m., EST
Course Block:

*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

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