BAN 704 - Applied Data Science

(3 units)

This course introduces several important modeling approaches for solving decision-making problems. The first part of the course focuses on statistical learning. The second part of the course introduces machine learning and decision-making under uncertainty.

Prerequisite(s): Admission to the MSBA program; BAN 701.

Grading Basis: Graded
Units of Lecture: 3
Offered: Every Spring

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. describe the importance of inference and prediction and distinguish them. Describe supervised and unsupervised learning methods and distinguish them. Interpret the model findings.
2. contrast different statistical and machine learning methods.
3. identify and describe the challenges in real-world data analytics projects.
4. identify and describe “good” vs. “bad” models by virtue of evaluation metrics.
5. identify a challenge/ shortcoming in each of the statistical methods discussed in this course AND find and describe a solution from the current research to address/alleviate the problem.

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