The M.S. in Business Analytics is a 32‐credit degree to be completed over the course of 10 months that will train professionals to develop skills necessary to gather, manipulate, understand and make use of big data in a business context. This set of skills touches on the four main phases of Analytics: data base management, consolidation and preparation for analysis, descriptive (understanding of data, what business inferences can be made from available data), predictive (use of data to forecast), and prescriptive (use of understanding of data and the forecasts to determine the best courses of action to achieve business goals).
As Business Analytics professionals, our students should be comfortable with data driven decision making. Our graduate should be able to demonstrate the ability to:
· Work with Big Data
From its initial acquisition, its storage, and final analysis, working with “big data” is a necessary skill in working with web applications, social media, advertising, and high‐frequency financial data. Students will be able to use big data tools and approaches with the analytical and visualization tools introduced in statistical decisions making and data visualization courses of the program.
· Develop and Use Data Mining/Machine Learning Methods and Software Tools
Data mining and machine learning rely on sophisticated algorithms to analyze data systematically to improve decision‐making. The data mining/machine learning courses will examine how data analysis techniques can be used to improve decision making. Students will study the fundamental principles and techniques of data mining and machine learning, and learn to examine real‐world examples and applications using software.
· Understand and Use Decision Models
Students will apply mathematical and statistical models to real world problems to make better managerial decisions. Using software tools designed for handling big data, students will obtain solutions to a variety of managerial problems. These techniques will be developed in the management science modeling and operations analytics courses of the program.
· Develop and Use Predictive Models
Students will be able to develop and use various predictive models and apply these methods to business problems. As new types of data observations that were unthinkable just a few years ago are becoming rapidly available, it gives rise to new and powerful predictive analytics driving emerging business models. These techniques will be developed in a sequence of statistics courses, including Applied Regression Analysis, Generalized Linear Models, and Time Series Analysis.
Sample Curriculum (Subject to Change)
MAS 631 – Statistics for Management Science (2 Credits)
MAS 632 – Management Science Models for Decision Making (2 Credits)
MAS 637 – Applied Regression Analysis and Forecasting (2 Credits)
MAS 639 – Data Acquisition, Preparation, and Visualization (2 Credits)
MAS 640 – Applied Time Series Analysis and Forecasting (2 Credits)
MAS 646 – Generalized Linear Models (2 Credits)
MAS 648 – Data Mining and Knowledge Acquisition (2 Credits)
MAS 649 – Big Data Analytics (2 Credits)
MAS 651 – Machine Learning (2 Credits)
BUS 641 – Business Analytics Capstone (2 Credits)
BUS 630 – Fundamentals of Economics, Accounting and Finance (4 Credits)
BUS 632 – Introduction to Strategy, Market and Management (4 Credits)
· Students wishing to obtain the Six Sigma certification must take MAS 633 and 634.
MAS 629 – SAS Programming for Business Analytics (2 Credits)
MAS 633 – Introduction to Quality Management (2 Credits)
MAS 634 – Administrative Systems for Quality Management (2 Credits)
MAS 635 – Design of Experiments (2 Credits)
MAS 638 – Management Science Consulting (2 Credits)
MAS 663 – Project Management and Modeling
BTE 601 – Programming for Distributed Systems (2 Credits)
BTE 620 – Database Development for High Performance Computing (2 Credits)
BTE 622 – High Performance Computing (2 Credits)
录取标准为GPA 3.0以上，托福IBT 94分以上或雅思 7.0分以上，如有疑问，请通过下方的联系方式进行咨询。