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 will 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.
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.
Admitted students must complete 30 prescribed credits (10 courses) and may attend
full-time or part-time. Most classes are in-person, with some offered online or in
a hybrid format.
Click on a course below to view its description:
REQUIRED COURSES
These are the list of courses that you are required to take.
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.
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.
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 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.
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.
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.
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.
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.
ELECTIVE COURSES
You will select two (2) elective courses from the list below.
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.
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.
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.
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.
Designed to accommodate independent research projects on an individual basis with
faculty guidance.
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.
Unlock the power of data-driven decisions
Master the skills to analyze, interpret, and apply data for smarter decision-making.
The Master of Science in Decision Analytics at Stony Brook University’s College of Business provides expertise in statistical
analysis, data visualization, and business intelligence. Through hands-on learning
and real-world applications, you'll develop the tools to solve complex problems and
drive strategic success. Whether you're looking to refine your analytical skills,
apply data-driven insights, or advance your career in a high-demand field, this program
provides the foundation for success.
Expertise
Develop Advanced Skills in Data Analytics
Application
Solve Complex Problems with Hands-On Learning
Growth
Expand Career Opportunities in a Data-Driven World
Take the next step toward a rewarding career by adding decision analytics expertise
to your skill set. Submit an application for the Master of Sciene in Decision Analytics
today and open the door to new opportunities.
Visit the Student Financial Services website for details on tuition rates and fees
for New York State residents and non-residents, both for full-time and per credit
rates. Tuition and fee billing rates are subject to change.
Visit the College of Business Graduate Admissions page to learn about criterial required
for admissions consideration to the MS in Decision Analytics program.
Take the first step toward advancing your career by starting your application for
the MS in Decision Analytics today. Gain the skills and knowledge needed to make data-driven
decisions and drive impactful business solutions.
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 meetingwith our graduate admissions and advising team.
