Introduction to Data Science

Professors: Osoba, Sanchez, and Kravitz
Units: 1.0
Elective Course
Concentration: Quantitative Methods
Prerequisite: Prior experience programming is recommended, but not required.


This 10-week course provides students with a foundation to confidently use a variety of different data sources and tools to address policy relevant questions. It provides students with hands-on experience using programming languages such as Python or R to import data, conduct exploratory analysis, construct data visualizations, and create predictive statistical and machine learning models. The course additionally introduces students to less traditional data sources, such as web search history using Google Trends and Google Correlate, and discusses the strengths and weaknesses of such data relative to more traditional data sources.

Class time is divided approximately equally between: (1) providing introduction and overviews of data science tools (2) discussing research papers and book chapters that use innovative data and (3) student presentation and discussion of assignments, which asks students to explore a data set of their choosing over the 10-week course using tools covered in class. Students are also provided with and assigned online programming instruction.