Master of Science in Business Analytics

Transcription

1 Toward A Model Curriculum for the Master of Science in Business Analytics by Charles K. Davis, PhD and Charlene A. Dykman, PhD

2 Proposed Degree Overview Master of Science in Business Analytics (MSBA) Five Semester, Lock-step, Part-time Program Designed for Working Professionals Nine Courses (Twenty-Seven Hours) Required Evidence of Strong Mathematics Ability Desirable Programming & Database Skills Also Desirable Two Years to Complete Including one Summer

3 Methodology for this Study 1. Conduct Extensive Literature Review on Big Data Analytics 2. Find & Document Big Data Analytics Masters Degree Programs 3. Evaluate the Curricula of Each of those Programs 4. Identify Common Topics among the Programs 5. Construct Lists of Key Big Data Analytics Topics 6. Refine Lists of Topics Using Results of the Literature Review 7. Establish Big Data Analytics Working Body of Knowledge 8. Design Graduate Courses Covering that Body of Knowledge 9. Define the Structure of an MS in Business Analytics

4 Body of Knowledge Topics 1.Decision Support & Intelligent Systems 2.Forecasting Overview 3.Optimization Overview 4.Basic Statistical Methods 5.Stochastic Modeling 6.Problem Solving Techniques 7.Research Methods 8.Modeling Markets 9.Modeling Operations 10.Modeling Supply Chains 11.Multivariate Statistics Overview 12.Multiple Regression 13.Principles of Analysis & Design 14.Basic Analytics Software Tools 15.Data Management 16.Business Intelligence 17.Data Mining 18.Agile Programming Methods 19.Advanced Analytics Software Tools 20.Financial Modeling 21.Overview of Econometrics 22.Pricing and Revenue Optimization 23.Interpreting Analytics Results 24.Communicating Analytics Results

5 Proposed Course Titles 1) Foundations of Business Analytics 2) Management Science 3) Model-Building Practicum 4) Prediction & Time Series Analysis 5) Basic Analytics Programming 6) Managing Big Data 7) Advanced Analytics Programming 8) Financial Analytics Practicum 9) Capstone in Business Analytics

6 Masters Degree Curricula Reviewed The University of Texas New York University Michigan State University Louisiana State University North Carolina State University The Stevens Institute Northwestern University University of Michigan - Dearborn Fordham University Drexel University DePaul University Arizona State University Imperial College London

7 REFERENCES Davis, G. B. (2002). Information Systems as an Academic Discipline: Explaining the Future. Journal of Information Systems Education, 4(4). Gorgone, J.T., Gray, P., et al. (2006). MSIS 2006 Model Curriculum and Guidelines for Graduate Degree Programs in Information Systems. Communications of the Association for Information Systems, 17 (1). Manyika, J., Chui, M., et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. Mckinsey Global Institute. Sircar, S. (2009). Buisness Intelligence in the Business Curriculum. Communications of the Association for Information Systems, 24 (17).

8 Appendix: Master of Science in Business Analytics OVERVIEW

9 First Fall Term Title: Foundations of Business Analytics (3 hours, required) Course Description: This course begins with a summary of the origins of Business Analytics as a field and progresses to a review of decision support and intelligent systems theory. The course also introduces model building and provides an overview of typical forecasting and optimization techniques used. Course Topics: Decision Support Systems, Intelligent Systems, Forecasting Overview, Optimization Overview Title: Management Science (3 hours, required) Course Description: This course introduces students to the field of Management Science, beginning with a review of basic statistical methods and stochastic modeling. Research methods and several of the more important t management science problem-solving l techniques are also explored. Course Topics: Basic Statistical Methods, Research Methods, Stochastic Models, Problem Analysis Techniques

10 First Spring Term Title: Model-Building Practicum (3 hours) Course Description: The focus of this course is the art and science of building applied mathematical models. It begins with an overview of techniques used to model complex markets and market segments, followed by an overview of techniques used to model both production and logistics. This course includes an independent modeling project conducted by each student. Course Topics: Modeling Markets, Modeling Operations, Modeling Supply Chains, Independent Research Project Title: Prediction and Time Series Analysis (3 hours, required) Course Description: This course begins with an introduction to multivariate statistics and associated statistical tools. The core of this advanced course is an in-depth examination of the multiple regression technique for forecasting time series. Students will learn how to use multiple regression to predict future trends. Course Topics: Multivariate Statistics, Multiple Regression, Forecasting Exercises

11 Summer Term Title: Basic Analytics Programming (3 hours, required) Course Description: This course begins with an introduction to systems analysis and design, focusing on the aspects of this discipline needed for Analytics work. The primary thrust of this course is the exposure to a series es of analytical a software tools that are commonly o used in this discipline and a set of lab assignments to reinforce student learning. This first course deals with the least advanced of these tools. Course Topics: Principles of Analysis & Design, Basic Analytics Software Tools, Lab Assignments

12 Second Fall Term Title: Managing Big Data (3 hours, required) Course Description: This is a course in data structures focusing on large scale databases and data warehouses, as well as aspects of data manipulation that characterize modern Business Analytics problems. Included will be current theory on business intelligence and data mining as well as an appropriate set of problems dealing with big data issues. Course Topics: Data Management, Data Warehousing, Business Intelligence, Data Mining, Practical Problems Title: Advanced Analytics Programming (3 hours, required) Course Description: This is the second in the analytics programming course sequence. This course begins with an introduction to agile programming techniques, an approach often utilized for such work. Then, the student t is exposed to a series of advanced d analytical l software tools with a set of lab assignments to reinforce this learning. This course culminates with a team project in forecasting. Course Topics: Agile Programming Methods, Advanced Analytics p g g g y Software Tools, Team Project Forecasting

13 Second Spring Term Title: Financial Analytics Practicum (3 hours, required) Course Description: The focus of this course is Business Analytics applied to finance and economics. It begins with an overview of techniques for econometrics and financial modeling. The majority of this course is an independent research study conducted by each student culminating in a comprehensive term paper on an aspect of Financial Analytics. Course Topics: Financial Modeling, Econometrics, Pricing and Revenue Optimization, Independent Research Project Title: Capstone in Business Analytics (3 hours, required) Course Description: The capstone for this program begins with a series of lectures about interpreting the results of analytic work and communicating results to management. These are crucial skills for success in this field. The majority of this course is devoted d to a major, unifying i case study in business analytics. Course Topics: Interpreting Analytics Results Accurately, Communicating Analytics Results Effectively, Unifying Case Study in Analytics

14 Dr. Charles K. Davis Cameron Endowed Chair & Professor Cameron School of Business 3800 Montrose Blvd. Houston, TX