DATA 401: Advanced Topics in Data Analytics

Data Analytics
Course in Data Analytics
Course Description:

Data Analytics 401: Advanced Topics in Data Analytics emphasizes the skills necessary to do predictive modeling of data. This is one of the most commonly sought-after skills in data science jobs, since it can help companies structure future investments, non-profits organize funding drives, or political candidates decide where to focus their get-out-the-vote efforts. The class begins with a comprehensive discussion on basic regression analysis and then moves on to more advanced topics in R like web scraping, mapping, textual analysis, and working with string variables. The course also features content about more advanced data visualization skills, including creating interactive data visualizations in RShiny.

Certificate students and individual course takers must complete three prerequisite data analytics courses before enrolling in this course. 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 2 (accelerated): Mar 15 – May 11, 2021
Fall 2 (accelerated): Oct 28 – Dec 21, 2021
2022 Term Offered:
Spring 2 (accelerated): Mar 14 – May 10, 2022
Prerequisites
DATA 101, DATA 210, DATA 310
Synchronous Session:
Weekly synchronous session required
Spring 2 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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