Co-Directors: Penina Orenstein, Ph.D.; Renu Ramnarayanan, Ph.D.
Advisers: Penina Orenstein, Ph.D.; Mark D. Schild, M.B.A.
Seton Hall’s M.S. in Business Analytics program empowers professionals who have an analytical bent to make a career in data-driven decision-making. The program creates well-rounded professionals with a solid foundation in business and in analytics. Our graduates will originate innovative ideas and possess the skills and knowledge to follow them through to execution.
Degree Requirements
Completion of the M.S. in Business Analytics requires a minimum of 30 credits of approved coursework, composed of 21 credit hours of required courses and 9 credit hours of approved IT or Quantitative Analysis electives.
Three restrictions apply: First, no course may be transferred if it has been applied to a prior degree. Second, a minimum of 24 credit hours of coursework must be completed at the Stillman School. Third, students may not repeat courses taken previously at the graduate or undergraduate level.
GPA needs to be 3.0 to maintain a position in the program.
Prerequisite Knowledge
Candidates for the M.S. in Business Analytics should have a quantitative background equivalent to that obtained through a basic course in business statistics as well as strong proficiency in the use of Microsoft Excel.
M.S. in Business Analytics Curriculum
The program balances courses in business processes with courses in exploratory and predictive analytics and covers everything a business decision-maker needs to know – from using R/Python, JSON/ XML and SQL, to examining business processes through data models, to extracting meaning from big, unstructured data.
Course List
| Code |
Title |
Hours |
| BSAN 7001 | Intro Data Analytics - Bus Int | 3 |
| BSAN 7011 | Exploratory Analytic-Visualztn | 3 |
| BSAN 7021 | Predictive Analytics | 3 |
| BSAN 7031 | Databases and SQL | 3 |
| BSAN 7041 | Business Process - Data Model | 3 |
| BSAN 7051 | Big Data Analytics | 3 |
| BSAN 9000 | Capstone Project | 3 |
| 21 |
| 9 |
| Project Management | |
| Cognition for Visualization | |
| Supply Chain Management | |
| Logistics and Operations in Supply Chain Management |
| Big Data, Analytics and Business Impact | |
| Management Science | |
| Data Engineering | |
| Text Mining | |
| Deep Learning | |
| Ethical Challenges of Big Data | |
| 9 |
| Total Hours | 30 |
