Introduction to Data Wrangling

3 units

Students studying applications of machine learning to problems arising in the social sciences must learn how to analyze data coming from a variety of sources. Moreover, such data may be flawed in a variety of ways, including missing or erroneous entries, redundant entries, or bias stemming from how the data was collected. This course is intended to provide an introduction to conducting research using methods from data science. We examine the following sources of data: static datasets, dynamic data accessed via an Application Programming Interface (API), web scraping as needed to harvest data where web pages contain desired data but no API is present, and surveys as needed to obtain data not already available. Students ingest data, perform analyses, and document their findings in a scientific and reproducible way, which allows the interspersing of data, code, analysis, and prose. This serves to document and make reproducible the scientific process carried out by the students. Students present their results to their peers and faculty with critical reviews of their presentations. Enrollment is limited to students in the DCDS program.