Introduction to Data Science for Business and Economics
BUSN 5000
3 hours
Introduction to Data Science for Business and Economics
Course Description
The modern world is awash in data. Harnessing data for decision-making begins with acquiring the raw information and ends with communicating the results of analysis. This course covers the data science skills necessary at every stage of the value chain: data transformation; descriptive, explanatory, and predictive analyses; and professional communication.
Athena Title
Intro to Data Science for Busn
Equivalent Courses
Not open to students with credit in BUSN 5000E
Prerequisite
BUSN 3000 or BUSN 3000E or BUSN 3000H
Grading System
A - F (Traditional)
Student Learning Outcomes
After completing this course, the student should understand how to acquire and prepare data for analysis.
After completing this course, the student should understand how to design reproducible data analyses.
After completing this course, the student should understand how to map business problems and policy questions to hypotheses about relationships in data.
After completing this course, the student should understand how to describe data and perform basic descriptive analysis.
After completing this course, the student should understand how to implement and interpret basic causal-inference research designs.
After completing this course, the student should understand how to implement and interpret basic machine-learning algorithms.
After completing this course, the student should understand how to communicate the results from descriptive, causal, and predictive analyses.
Topical Outline
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
