This 21-month full-time thesis-based M.S. program emphasizes theory, concepts, modeling approaches, modeling and analysis tools in the context of Business Analytics.
The primary objective of Master of Science in Business Analytics Program (MScBA) is to teach students descriptive, predictive, and prescriptive analytics tools that enable them to develop managerial insights for decision making from complex, large, and structured or unstructured datasets. The program welcomes applicants with quantitative backgrounds in management, economics, engineering, and natural and social sciences with a strong appetite for data analysis, modeling, and data-driven decision making.
MScBA is a rigorous two-year program with a master thesis. The first year is mainly dedicated to courses and seminars to cover the fundamentals of various descriptive, predictive and prescriptive analytics methods, while the second year is mainly dedicated to developing and defending a master thesis under direct supervisorship of our faculty members. The master thesis is expected to involve a business or policy problem and offer solutions by using various business analytics tools. The students will also have the opportunity to participate in the research projects offered at the Behavioral Analytics and Visualization Lab and other research programs conducted by faculty members. Upon successful completion of this program, the graduates are prepared to take the role of data scientist or business analyst in leading corporations in a wide range of industries or pursue doctoral studies.
The program seeks to achieve the following learning outcomes:
- Demonstrated understanding of data-driven decision modeling and analysis concepts and frameworks,
- Knowledge of and hands-on experience with fundamentals of business analytics, management information systems, statistical and prediction models,
- Ability to transform complex data into valuable insight and resulting value-adding actions,
- Skills in hands-on data-mining tools and techniques thereby leading to graduates that are competitive in the Analytics job market,
- Deeper expertise in a selected line of research using real data.
The required number of courses is 8, which is equivalent to 24 SU credits. After completing the 8-course, students will conduct thesis-based research over the Fall and Spring semesters of their second year.
| Fall Semester 1 | SU Credits | |
| BAN 500 | Introduction to Business Analytics | 3 |
| BAN 502 | Introduction to Decision Making | 3 |
| BAN 505 | Predictive Analytics | 3 |
| BAN 527 | Descriptive Analytics | 3 |
| BAN 599 | Graduate Seminars Course | 0 |
| Spring Semester 2 | ||
| Elective Course 1 | 3 | |
| Elective Course 2 | 3 | |
| Elective Course 3 | 3 | |
| Elective Course 4 | 3 | |
| Fall Semester 3 | ||
| BAN 600 | Master Thesis | 0 |
| Spring Semester 4 | ||
| BAN 600 | Master Thesis | |
| Course Code | Course Name |
| BAN 503 | Management Information Systems |
| BAN 504 | Data Mining with SAS Enterprise Miner |
| BAN 521 / OPIM 523 | Decision Models |
| BAN 522 | Revenue Management |
| BAN 523 | Group Decision Making under Multiple Criteria |
| BAN 524 / OPIM 506 | CRM using Location Intelligence |
| BAN 526 / OPIM 526 | Business Intelligence and Decision Support Systems |
| BAN 525 / ECON 501 | Microeconomics I |
| BAN 528 / ECON 502 | Microeconomics II |
| BAN 529 / ECON 506 | Econometrics |
| BAN 533 / IE 503 | Stochastic Processes |
| BAN 531 / IE 524 | System Simulation |
| BAN 537 / IE 527 | System Dynamics |
| BAN 535 / CS 515 | Neural Networks |
| BAN 532 / CS 512 | Machine Learning |
| BAN 539 / CS 525 | Data Mining |