Master of Science in Artificial Intelligence and Business Analytics

The MS in Artificial Intelligence and Business Analytics (MSAIBA) prepares graduates for leadership in data- and AI-driven decision-making. The program develops critical skills in applied artificial intelligence, data engineering, machine learning, and model-driven management, equipping students to design, deploy, and govern AI-powered analytical solutions in a business context. This STEM-designated program is designed to be completed either in one year's time (full time) or over two years (part time). The program may be taken either asynchronously online or in-person, and students can choose either one for any course. Students may opt to use their elective courses to earn a specialization that is tailored to their career interests.

Available specializations include: Business Intelligence, Financial Planning and Analysis, Healthcare, Leadership, Marketing Analytics, Quantitative Finance, and, for those who have an undergraduate degree in Accounting or the equivalent, Accounting.

Dolan's MSAIBA program is also available to students who wish to pursue it online from Shanghai, China. For further information on completing the MSAIBA from China and the Middle East, please contact Evelyn Zhang (yzhang2@fairfield.edu).

The overarching learning goals of the program are for students to be able to:

Goal I: Translate back-and-forth between messy real-world situations and tractable formal models, in problem formulation and solution interpretation, application, and communication.

Goal II: Fluently apply well-defined quantitative, mathematical, and AI techniques, including knowing when each one applies and when it does not, and testing such assumptions in the real world.

Goal III: Have both the technical competence and the confidence to both learn and apply novel technologies, including AI, as needed to solve business problems.

Fairfield University’s Dolan School of Business Master of Science in Artificial Intelligence and Business Analytics (MSAIBA) represents an evolution of the former Master of Science in Business Analytics (MSBA). Beginning in Summer 2026, the program was updated and renamed to reflect the growing integration of artificial intelligence within the field of business analytics and to better align with industry demands.

Students who enrolled in the MSBA prior to Summer 2026 may continue and complete their degree under the original MSBA curriculum. All students enrolling in the program from Summer 2026 onward will pursue the MSAIBA degree, which incorporates enhanced coursework in artificial intelligence alongside core business analytics competencies.

Requirements

The requirements for the MSAIBA fall into the broad categories of Essentials, Foundations, Professions, Electives, and the Capstone.

Course List
Code	Title	Credits
Essentials
These two Essentials courses are prerequisites for later courses. Either one or both may waived with successful completion of an online test-out exam. For students pursuing the Business Intelligence specialization, DATA 5400 counts as one of the requirements. For students pursuing the MSAIBA without a specialization, either one or both of these courses may count towards the three electives:
DATA 5400	Applied Business Statistics (no prerequisites)
DATA 5405	Programming Fundamentals for Business Analytics (no prerequisites)
Foundations		12
Each of these four Foundations courses are required for all MSAIBA students and form the basis and prerequisites for the Professions courses:
DATA 6500	Leading with Analytics and AI (no prerequisites)
DATA 6505	Data Engineering for Business Analytics (no prerequisites)
DATA 6510	Data Design and Visualization (no prerequisites)
DATA 6515	AI Ethics and Governance (no prerequisites)
Professions		6
Professions courses introduce students to the various fields and disciplines that use analytics in the real world as well as the skills most necessary for those professions. Students choose any two of the following courses to fulfill their Professions requirement:
DATA 6545	AI Systems for Business (prerequisite: DATA 6505)
DATA 6560	Sports Analytics (no prerequisites)
DATA 6570	Generative AI Applications in Business (no prerequisites)
Electives		9
Students are required to complete an additional 9 credits (3 courses) of graduate work. Students may choose elective courses either to fulfill the requirements of one of the specializations listed below, or to enrich their background in an area of interest. If no specialization is chosen, either or both of the Essentials courses DATA 5400 and DATA 5405 may count towards these electives. Otherwise, the courses must be at the 6500-level.
Capstone		3
Students must choose one capstone course after completing their Foundations and Professions requirements.
DATA 6520	Analytics Consulting and Strategy (prerequisite: DATA 6500)
DATA 6575	Autonomous and Agentic AI for Business (prerequisite: DATA 6545)
DATA 6900	Contemporary Topics Seminar (no prerequisites)
DATA 6999	Capstone: Business Analytics Applications (prerequisite: 18 or more credits of DATA courses at the 5000-level or higher)
Total Credits		30

Accounting Specialization

To be eligible to pursue this specialization, students must have an undergraduate degree (BS or BA) with a major in accounting or the equivalent. The equivalent of an undergraduate degree in accounting includes the successful completion of: intermediate accounting (six credits), advanced accounting (three credits), cost accounting (three credits), auditing (three credits), and U.S. taxation (three credits). Deficiencies will be handled on a case-by-case basis.

To complete a specialization in Accounting, students will take any three graduate Accounting or Taxation courses at the 6000-level for their required electives. Students trying to meet educational requirements for CPA certification are encouraged to consult with the Director of Graduate Accounting Programs in selecting their graduate Accounting or Taxation courses.

Business Intelligence Specialization

Course List
Code	Title	Credits
To complete a specialization in Business Intelligence, the following courses are required:		9
DATA 5400	Applied Business Statistics ¹
DATA 6530	Statistical Modeling for Business Intelligence
DATA 6540	Business Intelligence
Total Credits		9

¹: If DATA 5400 is passed-out, it can be replaced with any other DATA course.

Financial Planning and Analysis Specialization

Course List
Code	Title	Credits
To complete a specialization in Financial Planning and Analysis, the following courses are required:		9
ACCT 6500	Accounting Information for Decision-Making
FNCE 6500	Stakeholder Value
FNCE 6530	Corporate Finance
Total Credits		9

Healthcare Specialization

Course List
Code	Title	Credits
To complete a specialization in Healthcare, select three courses from the following:		9
NURS 7602	Healthcare Economics and Marketing
NURS 7605	Advanced Health Policy
NURS 7613	Finance and Quality Management in Healthcare Organizations
NURS 7614	Information Technology for Healthcare Improvement
Total Credits		9

Leadership Specialization

Course List
Code	Title	Credits
To complete a specialization in Leadership, select three courses from the following:		9
MGMT 6500	Leadership (prerequisite: MGMT 5400)
MGMT 6504	Managing People for Competitive Advantage
MGMT 6505	Human Resource Strategy - An Artificial Intelligence & Human Intelligence Approach
MGMT 6508	Strategic Management of Technology and Innovation: The Entrepreneurial Firm
MGMT 6530	Entrepreneurship
MGMT 6540	Cross Cultural Management and Sustainable Leadership
MGMT 6584	Global Competitive Strategy
Total Credits		9

Marketing Analytics Specialization

Course List
Code	Title	Credits
To complete a specialization in Marketing Analytics, the following courses are required:
MKTG 6520	Research for Marketing Insights and Decisions	3
MKTG 6580	Advanced Marketing Analytics	3
Select one course from the following:		3
MKTG 6525	Customer Experience
MKTG 6560	Category Management and Shopper Insights
MKTG 6570	Digital Marketing and Analytics with AI
MKTG 6583	Pricing Strategies and Analytics
MKTG 6590	Experimental Research
MKTG 6900	Contemporary Topics
Total Credits		9

Note: MKTG 5400 or its equivalent is a required prerequisite for all upper-level marketing courses.

Quantitative Finance Specialization

Course List
Code	Title	Credits
To complete a specialization in Quantitative Finance, the following course is required:
FNCE 6540	Investment Analysis	3
Select two courses from the following:		6
FNCE 6545	Portfolio Management
FNCE 6565	Derivative Securities
FNCE 6570	Fixed Income Securities
FNCE 6580	Financial Risk Management
FNCE 6595	Research Methods in Finance
Total Credits		9

Analytics

DATA 5400 Applied Business Statistics 3 Credits

Using spreadsheet software, this hands-on course teaches a variety of quantitative methods for analyzing data to help make decisions. Topics include: data presentation and communication, probability distributions, sampling, hypothesis testing and regression, and time series analysis. This course uses numerous case studies and examples from finance, marketing, operations, accounting, and other areas of business to illustrate the realistic use of statistical methods. Previously BUAN 5400.

DATA 5405 Programming Fundamentals for Business Analytics 3 Credits

This course introduces the fundamentals of programming with a focus on core computational concepts and AI-assisted coding techniques that apply across general-purpose programming languages. Students will begin by exploring program structure, control flow, and data manipulation, while learning to harness AI tools to enhance problem-solving, debugging, and code development. Through concise lessons, quizzes, and interactive coding challenges, the course demonstrates how programming languages such as Python and R, combined with AI-augmented development, are utilized in contemporary Business Analytics environments. The course concludes with a comprehensive project that showcases both programming competency and the effective integration of AI-driven coding support in real-world analytical workflows.

DATA 5410 Analytics Programming for Business 1.5 Credits

This course focuses on quantitative modeling and analyzing business problems using spreadsheet software, such as Excel and its add-ins. Topics include descriptive analytics, visualizing and exploring data, predictive modeling, regression analysis, time series analysis, portfolio decisions, risk management, and simulation. Business models relevant to finance, accounting, marketing, and operations management are set up and solved, with managerial interpretations and "what if" analyses to provide further insight into real business problems and solutions. Open to MS Management students only. Previously BUAN 5410.

DATA 6100 Fundamentals of Analytics 3 Credits

This is an introductory level graduate course focusing on spreadsheet modeling to analyze and solve business problems. Topics include descriptive analytics, data visualization, predictive modeling, time series analysis, and data mining. Contemporary analytical models utilized in finance, marketing, accounting, and management are set up and solved through case studies. Previously ISOM 6500.

DATA 6500 Leading with Analytics and AI 3 Credits

This course provides a broad overview to the analytics profession, with a focus on data driven leadership, hands-on skills in analytics and AI, and ethics. Starting with a foundation of analytical framing, the course moves on to more advanced topics like data visualization and summarization, descriptive and inferential statistics, spreadsheet modeling for prediction, linear regression, risk analysis using Monte-Carlo simulation, linear and nonlinear optimization, decision analysis, and data storytelling. The course includes an introduction to prompt engineering and other AI basics, as well as discussions on ethics in the profession. The course culminates with a group research project using curated big data datasets, as well as individual exercises in problem framing intending to be a component of a capstone experience. Previously BUAN 6500.

DATA 6505 Data Engineering for Business Analytics 3 Credits

This course introduces fundamental techniques for collecting, preprocessing, and transforming data for business analytics and AI applications. Students develop hands-on skills in modern data processing and data mining, with an emphasis on feature engineering—both traditional and multimodal. The course explores how to convert raw data into meaningful, analyzable forms that power advanced analytical and machine learning models. Key topics include handling diverse data types, integrating information from multiple sources such as text, images, and videos, and applying methods for data acquisition and cleaning. By the end of the course, students will understand how effective data preparation and feature design serve as the foundation for predictive, descriptive, and AI-driven analytics introduced in more advanced courses.

DATA 6510 Data Design and Visualization 3 Credits

This course introduces data design and management. Starting from the traditional relational data model and database fundamentals, the course offers a hands-on introduction to Structured Query Language (SQL) for defining, manipulating, accessing, and managing data, accompanied by the basics of data modeling including entity relationship modeling and diagrams. The course also offers an introduction to NoSQL and data warehousing approaches to handling Big Data. The course further touches upon how artificial intelligence is shaping the field, including query generation and database design from natural language questions. The course concludes with a comprehensive data warehousing project that gives each student the opportunity to integrate and apply the new knowledge and skills learned from this class. Previously BUAN 6510.

DATA 6515 AI Ethics and Governance 3 Credits

This course focuses on governance frameworks for AI systems, including transparency, accountability, data privacy, consent, fairness, and social impact. Students will critically analyze AI governance frameworks drawn from sources such as the EU AI framework, NIST, and explainable AI (XAI) literature. Topics include assessment of transparency and risk, and the formulation of policy and governance recommendations. The course includes weekly readings, quizzes, case studies, and hands-on workshops. The final project centers on designing an AI governance framework for a real or realistic AI deployment.

DATA 6520 Analytics Consulting and Strategy 3 Credits

Prerequisite(s): DATA 6500.

With the rise of analytics for cutting-edge business innovation, the industry needs business leaders who can solve an organization’s most important problems by asking and answering questions using data. These business consultants need to bridge both the data analytics and business fields. This class tries to provide a “real world” consulting experience through a project-centric experiential approach, in addition to case studies of analytics consulting and business problem solving using descriptive, predictive and prescriptive analytics. When possible, class projects will be client-driven using community partners. Students work in teams using analytics to answer the client’s current and important business questions using data. The students will approach these as business analytics consultants by using effective project management to gathering requirements, using continuous client engagement to deepen understanding of the problem, suggesting ways in which to explore the question and its possible solutions through data, running different data models to approach the solution, working with clients to come up with effective analytics strategies, making business presentations based on findings, incorporating the inevitable changes that come with real world projects, and recommending strategic solutions based on their findings.

DATA 6530 Statistical Modeling for Business Intelligence 3 Credits

Prerequisite(s): DATA 5400 or placement exam.

This course introduces analytical techniques used for decision-making under uncertainty. Topics include time series and other forecasting techniques, such as Monte Carlo simulation, to assess the risk associated with managerial decisions. Specifically, we will cover data collection methods, time dependent models and analysis, advanced solver, time series techniques, exponential smoothing, moving averages, and Box-Jenkins (ARIMA) models. Application examples include financial models - stock prices, risk management - bond ratings, behavior models - customer attrition, customer likes/dislikes, buying patterns - propensity to buy, politics - identify swing voters, and sales. Previously BUAN 6530.

DATA 6535 Advanced Sports Analytics 3 Credits

Sports analytics is transforming the way teams, leagues, players, coaches, referees, and fans perceive and appreciate their favorite pastimes and games, including major team sports such as baseball, basketball, football, soccer, cricket, and rugby, more individualized sports like tennis and golf, and brand-new innovations such as e-sports. In this course, students will gain experience in framing analytical questions in sports, discover and evaluate cutting-edge research and findings in sports analytics, develop hands-on skills in using and implementing sports analytics solutions, and learn how to communicate findings to a non-analytical audience in an impactful and actionable way. This course culminates in a scholarly sports analytics research paper.

DATA 6540 Business Intelligence 3 Credits

Prerequisite(s): DATA 6510.

Modernly, business intelligence has become far more interactive. This course provides an advanced application and overview of the new techniques for building interactive dashboards and tools now prevalent in this profession. Additionally, with data overload happening on every level, the importance of good data storytelling has soared. Using programming languages and environments such as Tableau and R, this course introduces students to the business intelligence profession and teaches the skills necessary to develop and deploy cloud-based interactive apps to assist in data and analytical storytelling, including insights into user interface design (UI) and user experience design (UX). The course concludes with a comprehensive project. Previously BUAN 6540.

DATA 6545 AI Systems for Business 3 Credits

Prerequisite(s): DATA 6505.

This course provides an advanced understanding of the principles and practices of data science and AI engineering, with a special focus on business applications. It covers the design, development, deployment, and maintenance of AI-powered systems across their full lifecycle. Emphasizing practical relevance, the course applies AI and data-driven tools to business analytics. Programming tutorials and case studies introduce topics such as AI lifecycle management, foundation models, and AutoML. Students are expected to actively participate in the course deliverables through independent assignments, lab work, and group projects. The course culminates with a final project in predictive analytics, as well as individual exercises in modeling and interpretation intending to be a component of an analytics capstone experience.

DATA 6550 Big Data Management and Data Ops 3 Credits

Prerequisite(s): DATA 6505 and DATA 6510.

This course introduces the fundamentals of Big Data management and its implementation in the public cloud. Topics include classic theories of data architecture, dimensional database design, data pipelines, and data governance, supplemented with the latest developments in the emerging field of DataOps. The theory is grounded with hands-on experience building databases and data pipelines with the Modern Data Stack.

DATA 6560 Sports Analytics 3 Credits

DATA 6570 Generative AI Applications in Business 3 Credits

Artificial intelligence is becoming far more prevalent in the business and analytics worlds, yet many analytics professionals are excluded from participating in this new wave because they lack the strong coding foundations that are typically needed to implement this new technology from scratch. However, recent advances in AI/ML have coincided with desktop and cloud tools that can be deployed far more easily to generate new models without complicated coding requirements. This course will teach students how to discover, use, and daisy-chain such tools to solve real-world business problems in ways that would otherwise be impossible.

DATA 6575 Autonomous and Agentic AI for Business 3 Credits

Prerequisite(s): DATA 6545.

This course introduces students to autonomous AI applications including agents. Fundamental knowledge, such as the architectures of the deep neural networks, extraction of high-level features representing unstructured data, backpropagation, and stochastic gradient descent, are also reviewed. Additionally, students get hands-on experience building autonomous AI models. Topics covered in this class can include model building and optimization, image classification, natural language processing, generative models, and so forth. These topics cover the foundations and the latest developments in the field of autonomous AI.

DATA 6900 Contemporary Topics Seminar 3 Credits

This course draws from current literature and practice on information systems and/or operations management. The topics change from semester to semester, depending on student and faculty interest and may include: project management, e-business, management of science with spreadsheets, e-procurement, executive information systems, and other socioeconomic factors in the use of information technology. Previously ISOM 6900.

DATA 6990 Independent Study 3 Credits

This course provides an opportunity for students to complete a project or perform research under the direction of an Information Systems and Operations Management (ISOM) faculty member who has expertise in the topic being investigated. Students are expected to complete a significant project or research paper as the primary requirement of this course. Enrollment by permission of the ISOM Department Chair only. Previously ISOM 6990.

DATA 6999 Capstone: Business Analytics Applications 3 Credits

Prerequisite(s): 18 or more credits of DATA courses at the 5000-level or higher.

This capstone course for the MS Business Analytics program is to be taken in the last term before graduation. The purpose is to apply and integrate knowledge and skills learned in the program (statistics, modeling, data management, data mining, etc.) to a live data analytics project. The course is project-based, with students collaborating on their work under the guidance of faculty members. Application areas and format of the projects may vary, depending on faculty, dataset, and budget availability. However, the work should be rich enough to demonstrate mastery of business modeling and technology, with each student making a unique, demonstrable contribution to completion of the work. Previously BUAN 6999.

A dual graduate business degree program allows students to pursue two graduate degrees, combining a Master of Business Administration (MBA) with a specialized Master of Science (MS) graduate degree in a specific field, or combining two specialized MS programs. The goal is to provide a broader skill set, enabling graduates to apply business knowledge in specialized industries or roles.

The advantage of dual degree programs is that they can be completed in less time than pursuing the degrees separately. These programs are ideal for individuals looking to expand their expertise across multiple disciplines, enhance career prospects, and increase their versatility in the job market.

Interested students will contact the Program Directors of each program to develop their dual degree plan of study.

The dual degree options include:

MBA/MS Dual Degree

Students will complete the seven core MS courses, five MBA subject area courses, and four MBA concentration courses. The MBA concentration will be in a different discipline than the MS program. The MBA concentration courses will count as MS electives. A minimum of 16 courses/48 credits is required.

Any prerequisite course for either the MBA or any of the MS programs will be required.

MS/MS Dual Degree

Students will take the seven core MS courses from the first program, the seven core courses from the second program, plus an elective. A minimum of 15 courses/45 credits is required.

Any prerequisite course for either MS program will be required.

The Dolan Career Development Center provides professional development services that enrich graduate students’ academic experiences and inspire tomorrow’s business leaders. For more information, reference the Career Development section of this catalog.