Contact
Whitman Graduate Programs, busgrad@syr.edu
Program Description
The MS in Business Analytics challenges students to develop an interdisciplinary understanding of the applications of analytics to the fields of accounting, finance, marketing, and supply chain management through techniques in data collection, data visualization, statistical and pattern analysis, and data mining. The curriculum leverages an understanding of business applications from the Whitman School of Management combined with in depth technical offerings of the School of Engineering and Computer Science and the School of Information Studies.
Degree Requirements & Learning Objectives
This is a 36 credit program that leads to a Master of Science in Business Analytics degree.
The MS Business Analytics curriculum is designed as a 36-credit program and normally requires two years or four academic semesters to complete on a full-time basis. It consists of four elements: 6 credits of required core courses; 6 credits of Analytics Applications courses; 6 credits of Analytics Depth or additional Analytics Applications courses; and 18 credits of Analytics electives. Students may select electives from other graduate programs in the University.
Course List Code | Title | Credits |
MBC 638 | Data Analysis and Decision Making (required) | 3 |
SCM 651 | Business Analytics | 3 |
| 6 |
| Accounting Analytics | |
| Marketing Analytics | |
| Financial Analytics | |
| Principles of Management Science | |
| 6 |
| |
| Machine Learning for Business | |
| Linear Statistical Models I: Regression Models | |
| Time Series Modeling and Analysis | |
| Customer Relationship Management with Systems Applications and Products | |
| Lean Six Sigma | |
| Scripting for Data Analysis | |
| Data Administration Concepts and Database Management | |
| Natural Language Processing | |
| Introduction to Data Science | |
| Applied Machine Learning | |
| Big Data Analytics | |
| Information Visualization | |
| Data Warehouse | |
| Text Mining | |
| Advanced Big Data Management | |
| Quantitative Reasoning for Data Science | |
| Research Methods in Information Science and Technology | |
| Statistical Methods in Information Science and Technology | |
| Simulation and Data Analytics | |
| Introduction to Database Management Systems | |
| Analytical Data Mining | |
| Introduction to Data Science | |
| 18 |
| |
| |
| Object Oriented Programming in C++ | |
| Introduction to Artificial Intelligence | |
| Artificial Neural Networks | |
| Object Oriented Design | |
| Explorations in Computing and Programming | |
| Strategic Content Management | |
| Introduction to Probability | |
| Statistical Methods for Data Science | |
| Mathematical Statistics | |
| Fundamentals of Data Science | |
| Introduction to Statistics | |
| Quantitative Analysis | |
| Predictive Analytics | |
| |
Total Credits | 36 |
Transfer Credit
Students can transfer a maximum of 6 credits of coursework. The credits must be graduate level taken from an AACSB accredited business school. A grade of “B” or higher is needed to transfer in the credits. The grade itself does not transfer.
Satisfactory Progress
Students are required to maintain a GPA of 3.0 or higher to meet degree requirements.
Students must pass a comprehensive exam at the end of the last semester of the program.