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Master of Science in Decision Analytics 

Unlock the Power of Analytics in Business Decision-Making

 A one-year, 30-credit stem designated degree program

The Master of Science in Decision Analytics (MSDA) is a STEM-designated full-time or part-time program that equips college graduates with the skills to solve complex problems and drive strategic decisions across all business sectors with applications in marketing, finance, human resources, and supply chain and operations.

The Ever-Growing Demand for Business Analytics Across Industries

The demand for business analytics professionals continues to grow across various industries as organizations recognize the value of data-driven decision-making and seek to gain a competitive edge.

Skills You'll Gain

  • Master statistical, optimization, and simulation  models for data-driven and informed decision-making.
  • Gain proficiency in machine learning and other artificial intelligence (AI) techniques for addressing business challenges across diverse sectors.
  • Become an expert and a persuasive communicator, ensuring your data-driven solutions to business problems are clearly understood and showcased to stakeholders.
  • Showcase your decisions in writing and captivating oral presentations.
Application Deadlines
  • Fall 2024: July 1

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CURRICULUM

Admitted students will take a prescribed 30 credits (10 classes); students may attend full-time or part-time.
The majority of classes will be in-person with some online and/or hybrid classes.

Required Courses

 

COURSE TITLE

DESCRIPTION

Data Analysis and Decision Making

An introduction to statistical techniques useful in the analysis of management problems. We motivate each topic by managerial applications, and we analyze actual data sets using modern statistical software. Topics include probability estimation, hypothesis testing, and regression analysis.

Business Analytics

An introduction to mathematical models useful in the analysis of management problems. We motivate each topic by managerial applications, and we analyze problems using modern software. Topics include forecasting, linear, nonlinear, and integer optimization, simulation, Markov processes, decision analysis, and multi-criteria decision making.

Data Mining for Business Intelligence

The recent advances in the Internet and information technologies have resulted in an explosion of demand for big data analytics. The importance of data mining has already been recognized widely in the industry including many business areas, such as marketing science, financial analysis, and corporation management. In this course, we will be focusing on both key concepts and models of data mining and their implementations based on real-world data in business. Students will learn to process data using Excel, and apply data mining models using Weka, a data mining software.

Database Management 

Database processing is the foundation upon which all current applications rely and represent the repositories of business intelligence that play a crucial role in the strategic success or failure of a corporation. Even though they vary in size, complexity and organizational scope, there is an underlying common database engine that can be used to manipulate and analyze the stored information. The purpose of this course is to introduce the business professional to the fundamental concepts of database creation, design, application integration, maintenance, management and subsequent analysis.

Supply Chain Management and Analytics

Businesses engage in a diverse set of activities in their daily operations including production planning, resource procurement, inventory management, distribution, and interaction with other firms. The goal of supply chain management is to maximize the economic value of these activities through system level coordination. A successful supply chain streamlines the flow of materials, goods, information, and capital along each component of the supply chain.

Advanced Data Analysis and Decision Making

By successfully completing this course, the student will have an understanding of the ways in which advanced statistical methods are used to address significant decision-making problems as they arise in the business setting. Specifically, the student will understand the various ways in which decision problems can be formulated and solved and how to deal with violations of the assumptions commonly found in standard methods. The student will have a greater understanding of multivariate models and ways to build them, and how to handle data collected over time in looking for trends and in making predictions.

Risk and Uncertainty Analysis

This is a hands-on course on computer simulation and other probabilistic modeling approaches to analyze and improve business, service, and manufacturing systems that are subject to risk. The course takes the perspective of the consultant whose job is to analyze managerial decision based on imperfect observations and unknown outcomes to understand the behavior of the system and explore the effects of alternative decisions.

Decision Support Systems 

An advanced project-oriented course focusing on the interrelationships among management information systems, statistics, and management science. Both model-driven and data-driven decision support systems will be considered. Students will identify an appropriate business application, select suitable management science and statistical methodologies, build the required information system, and demonstrate how their decision support system addresses the stated management problem.

RESTRICTED ELECTIVE COURSES

Select two from the list below.

 

Advanced Analytics

This course introduces students to challenging business problems in distribution, routing and scheduling, and to the solutions strategies for such problem via discrete optimization. The topics include integer programming techniques such as cutting plane and branch and bound, special purpose algorithms for distribution and network problems, and heuristic optimization techniques for combinatorial optimization, such as Simulated Annealing, Tabu Search, Evolutionary Algorithms, Ant Colony Optimization.

Data Analysis for Finance

Recent innovation of information technology along with the fast growth of applications on the Internet have resulted in an explosion of financial data, new ways of data collection and storage, as well as additional opportunities for business and research based on the data. This course enables students to analyze financial data based on traditional financial models. The major topics include asset pricing, capital budgeting, risk management, pension fund management, portfolio analysis, and stock hedging. Students will learn (review) the models with a focus on their implementation using Microsoft Excel, Matlab, or other programming languages. In addition, the basic statistical models, such as regression, time series models and probability models will be used. ¿Big Data¿ (data mining) technology will be introduced with a focus on financial data analysis. The main topics include classification, clustering, association analysis and anomaly detection. The key objectives of this course are: (1) to review the classical financial models and statistical models; (2) to teach the concepts of data mining with a focus on financial applications; (3) to provide students extensive hands-on experience in applying the concepts in financial data applications.

Project Management

This course will explore the theory and practice of managing a project. We will examine the various tools that are available to monitor and measure managerial tasks and to define common business processes. Every aspect of business entails the execution of a series of defined tasks and the associated allocation of corporate resources. From developing new products to implementing customer loyalty programs, managers must understand business processes including their associated tasks, inter-relationships and transformations. Project management involves three primary activities: defining manageable tasks, mapping their logical flow, and creating an implementation process. In the course, we will explore ways to manage these functions successfully to increase the probability of achieving desired results. We will use the latest software tools including: MS Project, MS Visio, @Risk Project Simulation, Business Plan Pro 2007, WIP Information System - online and C-Commerce tools such as Instantstream.

Digital Marketing

Marketing on the internet is constantly changing. This course will give you a theoretical and practical understanding of different digital marketing activities, current trends and changes, and the skills to perform vital daily digital market functions. We will cover Search Engine Optimization and Search Engine Marketing (SEO/SEM), Email Marketing, Social Media Campaigns, Reputation Management and E-mail marketing. By the end of the course, students will have earned multiple certifications, for example Google Ads, Google Analytics, Hubspot, and any additional certifications as is consistent with industry best practices at the time. All of these certificates are well-respected and regarded in industry and will place students in a good position for the Digital Marketing job market.

Individual Directed Research in Business

Designed to accommodate independent research projects on an individual basis with faculty guidance.

Business Internship

An academic internship is a form of experiential education that integrates knowledge and theory learned in the classroom with practical application and skill development in a professional setting. An integral component of the experience that distinguishes it from other types of work is one or more forms of structured and deliberate reflection based on predetermined learning objectives.

 


Have More Questions?

We have compiled a list of frequently asked questions for applicants. If you need academic guidance and assistance with the application process, reach out to our Office of Student Services (OSS). For questions about which graduate program is the best for you, please contact us to schedule a meeting with our graduate admissions and advising team.  

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