Course Details

Making Business Decisions with Big Data
1.0 CR
Yes (may vary by section)
Big data are central to business decisions today. This class teaches the methods for the rigorous analysis of big data that can be used to obtain insights for business. It takes a practical view of statistics and big data analysis and provides students with a range of state-of-the-art statistical and machine learning tools to address economic questions. In the real world, one has to analyze data for different types of decisions and situations, and is rarely in the position of choosing his/her ideal data or setting. The quality of the data and the method with which they can make inferences vary greatly across contexts. Moreover, handling big data requires programming sophistication using statistical software. The ultimate goal of this class is therefore to master a set of analytical tools and obtain confidence and competence in statistical programming. In order to maximize the amount and joy of learning, the course is case-focused and most of the analysis will be conducted in groups in class. Using a fun and hands-on approach, students build on the foundational tools they obtained in the Business Analytics courses, and learn advanced applications by working with big data projects in class in groups. Students will spend several weeks replicating famous empirical analyses with big data from the real world. The replication exercises will be guided by the instructor and TAs and done in class in groups. After the analysis, students will interpret the results, assess their credibility and applicability to the larger economic question which motivated the analysis, and present these findings to the class. This class aims to teach three complementary sets of tools: 1) big data creation; 2) big data management and 3) establishing causal relationships with big data. Data creation provides an overview of the new tools for creating big data, such as machine learning techniques, web scraping, the creation of text data for statistical analysis, and the creation of geographic data using spatial software. Data management provides tools for transforming big data into manageable samples, including new machine learning techniques as well as important traditional tools such as merging different datasets. The final component focuses on establishing causal relationships with the data. It complements the first two components and aims to provide students with a good sense of: which questions can be asked of the data given the economic question and data availability, which method to use, and the advantages and limitations of the chosen method. Some examples of the topics that we will study are: fame and political success, trade wars, air pollution and real estate prices, Chinese imports and the U.S. economy, or the Mexican drug war. By the end of the course, students will have had hands-on practice using a wide array of empirical estimation methods in a wide range of applications and big data types such as individual-level and firm-level data, cross-country data, numeric data, survey data, administrative data, text data and spatial data. The class will discuss a variety of new software in the context of applications such as ArcGIS, R, Python, SAS, but students are only required to program in Stata. The high level of competency in Stata programming that students achieve by the end of the course can be transferred to the programming of other statistical software. This class requires students to have taken business analytics and prepares students for indepen
Diversity, Equity, Inclusion Pathway
Economics Major
Management Science Major
Winter 2023

CH (Chicago)
EV (Evanston)
All Students: (DECS-431-0 OR DECS-435-0 OR DECS-440-0)