Curriculum
The Master of Science in Business Analytics (MS-BA) program is a sequential program designed to help you gain deep, specialized knowledge in order to use analytics to improve business processes. The curriculum focuses heavily on applied analytics with deep dives into real data sets. You will take each class in a set schedule, leading to a tight-knit class. You will learn from one another as you gain new knowledge and work in teams on applied problems.
At the start of the program, MS-BA Tempe students will declare a degree track. You can choose to do the two semester Big Data track, or you can declare a 3rd semester of study and pursue a 16-month track in one of four areas: Cloud Computing and Tech Consulting, Fintech, Marketing Analytics, or Supply Chain Analytics. For students who start MSBA in the 2024-2025 academic year, the Big Data track requires completion of 30 credits hours. Completing a 3rd semester track includes 30 credits, plus 5 additional track courses including a Career course.
Tempe students who start the MS-BA program in fall and select a 3rd semester track are eligible for summer CPT and a related summer internship opportunity. Spring start track students are not eligible for CPT or a summer internship.
Note: Registration for all degree courses is completed by the Program Operations team. Curriculum is subject to change.
Course Descriptions
CIS 505 |
Enterprise Data AnalyticsEnsuring the foundational understanding of contextualized analytics within the business enterprise continuum by covering how data flows and is managed across the landscape of enterprise business processes. |
SCM 516 |
Descriptive and Predictive AnalyticsProvides a survey of concepts, structure and analytical tools that lay the foundation for employing quantitative techniques (descriptive and predictive) to gain insights that help decision makers make better decisions. Familiarizes students with descriptive statistics, probability and probability distributions, confidence intervals, hypothesis testing, linear regression, logistic regression and forecasting methods. |
CIS 591 |
Programming for AI and Data Analytics in BusinessProvides a foundation in programming fundamentals, the skills to combine and manipulate structured and unstructured data, and the ability to summarize, visualize, and draw insights from the data. |
CIS 508 |
Machine Learning in BusinessCharting a roadmap for data-driven decision making and getting a practical understanding of how IT tools and techniques can allow managers to extract predictive analytics and patterns from primarily numeric data. |
SCM 518 |
Analytical Decision ModelingExplains the skills and knowledge necessary for mastery of the use of quantitative modeling tools and techniques to support a variety of business decisions. Also explores deterministic optimization techniques, including linear programming, nonlinear programming, integer programming; network models and a brief introduction to metaheuristics. Covers the use of these models for a variety of common business problems. Practical application of these models uses Excel and standalone software. Also studies how to ensure that these solutions work in a wide variety of situations (what-if analysis). |
SCM 517 |
Business Process AnalyticsAddresses the use of analytics tools and techniques to enhance the ability of quality management approaches to improve processes. Introduces modern quality management approaches including six sigma and design for six sigma. Covers the define, measure, analyze, improve and control (DMAIC) improvement cycle: the core process used to drive six sigma projects. DMAIC refers to a data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. Provides an analytics roadmap to help users work through the DMAIC problem-solving process. |
CIS 509 |
Analytics for Unstructured DataExplores how to support informed decision making and extract predictive analytics and patterns from nonnumeric data by leveraging tools and techniques to analyze unstructured data. |
SCM 519 |
Quantitative Risk ManagementAddresses the skills and knowledge necessary to model situations where uncertainty is a major factor. Models include decision trees, queuing theory, Monte Carlo simulation, discrete event simulation, and stochastic optimization, along with application for solving a wide variety of common business problems. |
Elective |
Students select one 3 credit course from a department list of approved electives.. |
SCM/CIS 593 |
Applied ProjectThe Applied Project is the culminating experience of the program, in which student teams study a problem in a domain where analytics solutions have not yet advanced to a point of wide-scale adoption. The students gain real-world experience through projects that address an important new frontier of an organization’s analytics deployment. Students will be asked to identify appropriate analytics frameworks, models, and tools to make data-driven conclusions and discoveries. In addition, students will develop and advance their communication skills and leadership abilities. |
WPC 584 |
InternshipRequired for students following the internship track within the program. |