Borzou Rostami, PhD
Spring Term 2026 (1950)
MMA 610A - Analytics Capstone Project
0 units (fi 12)(SPR/SUM, 3-0-0)
This course represents the apex of the MMA program, extending over two semesters, and offers students an immersive, real-world experience in analytics. The Analytics Capstone Project serves as a significant demonstration of the students' analytical skills and their capacity to make data-informed decisions in intricate business environments. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.
PRA C01 (31401)
2026-05-05 - 2026-06-30
TR 09:00 - 12:00
Summer Term 2026 (1960)
MMA 610B - Analytics Capstone Project
6 units (fi 12)(SPR/SUM, 3-0-0)
This course represents the apex of the MMA program, extending over two semesters, and offers students an immersive, real-world experience in analytics. The Analytics Capstone Project serves as a significant demonstration of the students' analytical skills and their capacity to make data-informed decisions in intricate business environments. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.
PRA C01 (40894)
2026-07-07 - 2026-07-30
TR 09:00 - 12:00
Fall Term 2026 (1970)
MMA 600 - Coding Bootcamp
0 units (fi 3)(FIRST, 18 H 2W)
Two-Week Kick Start Bootcamp: Embark on a seamless learning journey as students engage in a well-rounded experience to master two essential programming languages - Python and R. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.
LECTURE A01 (58838)
2026-08-10 - 2026-08-20
MTWRF 09:00 - 16:50
LECTURE A02 (58839)
2026-08-10 - 2026-08-22
MTWRF 09:00 - 16:50
MMA 602 - Machine Learning For Business I
3 units (fi 6)(FIRST, 3-0-0)
The goal of the Machine Learning for Business course is to utilize machine learning techniques to transform raw data into valuable insights that can inform business strategies. This course demands a solid grasp of technical data handling methods as well as business goals. It involves an overview of various machine learning approaches, such as supervised and unsupervised learning, and their practical uses in business scenarios. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.
LECTURE A01 (58842)
2026-09-01 - 2026-12-08
MW 09:00 - 10:20
LECTURE A02 (58843)
2026-09-01 - 2026-12-08
TR 09:00 - 10:20
Winter Term 2027 (1980)
BUAN 423 - Prescriptive Analytics
3 units (fi 6)(EITHER, 3-0-0)
Prescriptive analytics involves the use of data, mathematical models, and algorithms to identify optimal solutions for achieving organizational goals. This process builds on descriptive and predictive analytics, going beyond the interpretation of past events and the forecasting of future scenarios to also provide advice on the most effective actions to meet business objectives. Students acquire the skills to convert complex business problems into mathematical models, and employ Python programming and commercial solvers to derive optimal decisions. Evaluation components will consist of assignments, case studies, group projects, and two midterm exams. Prerequisite: OM 252. Not open to students with previous credit in OM 423.
LECTURE B01 (80345)
2027-01-04 - 2027-04-09
MW 15:30 - 16:50
LECTURE B02 (82404)
2027-01-04 - 2027-04-09
MW 12:30 - 13:50
OM 623 - Prescriptive Analytics
3 units (fi 6)(EITHER, 3-0-0)
Prescriptive analytics involves the use of data, mathematical models, and algorithms to identify optimal solutions for achieving organizational goals. This process builds on descriptive and predictive analytics, going beyond the interpretation of past events and the forecasting of future scenarios to also provide advice on the most effective actions to meet business objectives. Students acquire the skills to convert complex business problems into mathematical models, and employ Python programming and commercial solvers to derive optimal decisions. Evaluation components will consist of assignments, case studies, group projects, and two midterm exams. Prerequisites: OM 502.
LECTURE B01 (81467)
2027-01-04 - 2027-04-09
MW 15:30 - 16:50