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BUSN 5000 Fall 2026

Data Science for
Business and Economics

Turn raw data into clear evidence, credible explanations, useful predictions, and decisions you can defend.

Class
TR, 9:55 and 11:35 a.m.
Location
Amos B010
Instructor
Chris Cornwell

About the course

From information
to insight

The modern world is awash in a seemingly unlimited amount of data. Harnessing these data for decision-making begins with acquiring the raw information and ends with communicating the results of analysis.

Along the way, data must be transformed for analysis and statistical methods matched to the task at hand. BUSN 5000 develops the skills needed at every stage of that value chain: data transformation; descriptive, explanatory, and predictive analysis; and professional communication. We organize that work as ATAC: acquire, transform, analyze, communicate.

Acquire Transform Analyze Communicate

Course objectives

What you will learn

By the end of the course, you should understand how to move deliberately from a business or policy question to a reproducible analysis and a clear account of what the evidence shows.

  1. Acquire and prepare data for analysis.
  2. Design reproducible data analyses.
  3. Map business problems and policy questions to hypotheses about relationships in data.
  4. Describe data and perform basic descriptive analysis.
  5. Implement and interpret basic causal-inference research designs.
  6. Implement and interpret basic machine-learning algorithms.
  7. Communicate results from descriptive, causal, and predictive analyses.

Topical outline

The arc of the semester

Two connected parts build from the fundamentals of learning from data to tools for explaining outcomes and making predictions.

Part I

Transformation to Analysis

  1. Data fundamentals
  2. Beginning to learn
  3. Models for exploration
  4. Making inferences
  5. Measurement error, sample selection, and confounding
  6. Bayesian approach to learning from data

Part II

Explaining and Predicting

  1. Regression fundamentals
  2. Potential outcomes and causal inference
  3. Regression discontinuity
  4. Difference in differences
  5. Prediction with regression
  6. Introduction to machine learning

Questions?

Start with the course materials.

The syllabus, weekly schedule, and project guide are the authoritative sources for Fall 2026.

Office hours
Wednesday, 2:00–3:00 p.m.
and by appointment

Email
cornwl@uga.edu