Economics + Data

Like many other fields of study, economics has benefited from the growing availability of data. Unlike many other fields, economics has long been a quantitative science and economists have been developing tools to bring data to bear on economic theory for decades.

For economists, data skills are a "force multiplier." The economic concepts you have mastered in your studies are more valuable when you can use data to both communicate and validate those ideas. No matter where your path may take you after graduation, a mastery of economic decision making and data analytics will make you more productive—and more valuable.

What can you do with these skills? Are they useful in the "real world?" What have students done after taking this class?

Have you finished my course and want to learn more? What tools are people using in industry?

Courses to consider

The economics department offers an array of courses that develop the tools and techniques needed for serious quantitative analysis. The economics advising office, and your professors, can help you determine which courses are best for you. Advising drop-in hours are T–F 10-noon and you can schedule appointments through Starfish.

Here are some ideas:

  • Statistics (ECON 310, Measurement in Economics): Basic measurement underlies all quantitative endeavors. A thorough understanding of probabilistic concepts (mean, covariance, Bayes' rule, …) is a good place to start.

  • Econometrics (ECON 400, Introduction to Applied Econometrics; ECON 410, Introductory Econometrics): Econometrics is the foundation for quantitative analysis. These courses focus on estimating economic models in unbiased and consistent ways.

  • Forecasting (ECON 460, Economic Forecasting): Time-series forecasting is widely used in finance and policy making. How fast will GDP grow next year? Employment? Inflation?

  • Data Analytics (ECON 570, Fundamentals of Data Analytics for Economists): This course covers the nuts and bolts of working with data in the "real world." The course covers basic data programming with a focus on data wrangling and visualizations.

  • Spatial Analysis (ECON 690, GIS Analysis and Big Data): The emphasis is on spatially detailed data, which describes the who, what, when, and where of human behavior. Learn to use Geographic Information Systems (GIS) software to create, organize, evaluate and visually represent spatial data.

  • Machine Learning (ECON 695, Topics in Economic Data Analysis): The course introduces models from the "statistical learning" field. Statistical learning, compared to econometrics, tends to focus more on prediction than inference but the two are complements.