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Data Science for Economists

Subject code


Course Number



Department/Program Faculty

Course Long Title

Data Science for Economists

Cross Listed Courses


Economics is at the forefront of developing statistical methods for analyzing data collected from uncontrolled sources. Because econometrics addresses challenges such as sample selection bias and treatment effects identification, the discipline is well-suited to analyze large or unstructured datasets. This course introduces practical tools and econometric techniques to conduct empirical analysis on topics like equality of opportunity, education, racial disparities, and more. These skills include data acquisition, project management, version control, data visualization, efficient programming, and tools for big data analysis. The course also explores how econometrics and statistical learning methods cross-fertilize and can be used to advance knowledge on topics where large volumes of data are rapidly accumulating. We will also cover the ethics of data collection and analysis. Prerequisite(s): ECON 255 and ECON 260 or 270.

Modes of Inquiry

Quantitative and Formal Reasoning [QF]

Writing Credit

No writing credit

Departmental Course Attributes - Major/Minor Requirements

(DCS: Praxis)

INDS Program Relationship

IDDC - DCA Program

Class Restriction

Exclude First Years

Offering Frequency

Normally offered every year