BZAN3307 · Business Analytics
Carroll School of Management
Machine Learning and Artificial Intelligence won't replace managers in the near term, but managers who use ML and AI well will replace those who don't. Organizations now have too much data and insufficient time for managers to consume data only in spreadsheets. Instead, the future of work involves managers designing models that, for example, segment customers, forecast sales, schedule preventative maintenance, or predict markets. This course addresses both the technical and managerial aspects of these applications. Technically, students use Python to create, evaluate, and tune multiple practical models (e.g., classifiers, trees, neural networks) in supervised and unsupervised machine learning contexts. Managerially, this course examines how organizations create value through AI applications.
Course experience
Averages use the original five-point historical evaluation scale.
Organization
4.5 / 5
How well the course was organized
Challenge
4.7 / 5
How intellectually challenging students found it
Attendance
4.3 / 5
How necessary attendance was
Assignments
4.3 / 5
How helpful assignments were
Weekly effort
~3.5
hours per week
Estimated from the original workload response buckets. Individual sections may differ.
Instructor options
Ratings below reflect only recovered evaluations connected to this course.
David J. Mastrocola Dean's Faculty Fellow, Business Analytics · CSOM
4.8 / 5
4.5 / 5
Assistant Professor, Business Analytics · CSOM
4.0 / 5
3.8 / 5
Professor of the Practice, Business Analytics · CSOM
4.4 / 5
4.3 / 5
Student perspective
Recovered and new anonymous comments are shown as written and may reflect different course formats.
May 2, 2025 · Historical comment
Professor Kim is so kind! He is so excited about machine learning and AI and does a pretty good job explaining. He is great outside of class if you are confused as well. However, sometimes exam and HW feedback is slow.
Across time
Section-level results available in the recovered archive.
Spring 2025
3 sectionsFall 2024
3 sectionsSpring 2024
2 sectionsFall 2023
3 sectionsSpring 2023
1 sectionFall 2022
3 sectionsSpring 2022
1 sectionFall 2021
1 section