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The Business Intelligence program offers an opportunity to professionals looking to incorporate quantitative analytical skills and business better practices within their respective fields.
Consisting of 30 credits (no additional thesis required), the MSBI program not only gives you the education necessary to navigate current analytical technologies and tools, but it also provides a platform in which to develop expertise, hone your critical thinking abilities and your communication skills. Courses in the MSBI program are based on analytical tools and methods used in industry.
The rigorous curriculum of the MSU Denver Master Program in Business Intelligence is designed to prepare you for the challenges of incorporating data driven intelligence in the realm of your profession.
Fall 2026 Application Deadline is August 1st.
Apply by August 1st for Fall 2026
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Email Erin Rogers, MSBI Program ManagerBenefits of Earning your Business Intelligence Master Degree at MSU Denver
- Make a great financial decision. MSU Denver is the value leader among Business Intelligence/Analytics programs in Colorado.
- Take classes at times convenient for working students. Most MSU Denver MSBI classes are offered in the evening one day a week or online.
- Conduct academic research working with a CIS faculty member.
- Study with professors who have work-experience in the business world and are experts in their disciplines. Classes are kept small to provide you with one-on-one engagement opportunities, assistance and advice.
- Gain job search assistance from MSU Denver’s C2 Hub (College to Career Hub), which brings companies and organizations to campus interested in recruiting MSU Denver students.
Why Choose MSU Denver?
Eligibility Requirements
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Applicants must hold a bachelor’s or graduate degree from an accredited institution.
- In addition, applicants must meet one of the following:
- Successful completion of at least 15 credits in an accredited institution in relevant coursework such as statistics, calculus, computer science, information systems, or related quantitative fields (if not sure, please contact department at [email protected]), or
- Submission of GMAT or GRE scores
Applicants are required to submit:
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Official transcripts from postsecondary institutions
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A current CV or résumé
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A personal statement explaining academic and professional goals and motivation for pursuing the MS in Business Intelligence at MSU Denver.
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Documentation of relevant work experience, if applicable
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GMAT or GRE scores, if using test scores to satisfy the admission eligibility requirement
Applications are reviewed holistically, with emphasis on academic preparation and alignment between the program and the applicant’s career objectives.
MSBI Required Courses
BI 5010 – Business and Economics for Professionals
In this course, students gain essential knowledge in economics and business, analyzing consumer and producer decision-making, interpreting economic indicators like GDP, inflation, and unemployment, and understanding key business functions and strategic planning. Students learn to interpret financial statements, apply basic accounting principles, and use financial analysis techniques. They also explore marketing concepts, conduct market research, examine consumer behavior, and study organizational behavior, with a focus on leadership and team dynamics.
| BI 5050 – Programming and Spreadsheet Modelling
In this course, students build essential programming skills and master advanced spreadsheet techniques for real-world business applications. Beginning with core programming concepts such as variables, conditionals, loops, and functions, students learn data manipulation within spreadsheets. They explore data wrangling, pivot tables, queries, and analytical tools, gaining hands-on experience with arrays, objects, forms, error handling, and external data management. Through practical applications, students create data models and generate detailed reports. By the course’s end, students are proficient in using spreadsheets to analyze data, solve business challenges, and present actionable insights. |
MBA 5210 – Knowledge Discovery Using Business Analytics
This course presents students with tools and concepts from each of the three areas of Business Analytics-Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics. Descriptive Analytics addresses descriptive statistics and the exploration of data. Predictive Analytics covers regression analysis, model building, time series analysis, forecasting and Analysis of Variance. Prescriptive Analytics addresses decision making under uncertainty and risk, linear optimization, integer optimization nonlinear optimization, and simulation modeling. A variety of software tools are used to analyze data and solve decision-making problems.
BI 5060 Database Management and Warehousing
Prerequisite: BI 5050
In this course, students learn both foundational and advanced database concepts, from design to management. They start by creating database designs from user requirements and implementing them with up-to-date database tool(s). Key topics include distributed database management, concurrency control, data warehousing, and data mining. As students advance, they develop skills in complex SQL queries and use PL/SQL for building end-user applications. Hands-on experience includes designing, developing, and prototyping functional applications, equipping students with practical skills for real-world database management.
BI 5100 Data Visualization and Dashboard Design
Prerequisite: BI 5010, BI 5050, MBA 5210
In this course, students develop comprehensive data visualization skills to analyze and communicate insights effectively. They learn to map data to visuals using best practices, assess coordinate systems, scales, and visual encodings for various data types. The course covers techniques for creating statistical graphics that represent distributions, proportions, and hierarchical data. Students also explore principles of figure design, visual perception, and avoiding misleading visuals, along with crafting compelling narratives and dashboard presentations that meet audience needs and address business challenges.
MBA 5220 Practical Business Analytics
This course introduces the role of business analytics in organizations using a variety of business analytics methods. This course applies practical methodologies, strategies, and best practices for performing descriptive, predictive, and prescriptive analytics. In order to support the overall business analytics framework and methodology, this course also includes the use of enterprise level analytics tools and systems.
MBA 5230 Data Mining for Business Intelligence
This course provides students with a theoretical and practical understanding of data mining concepts and techniques and hands-on experience in applying these techniques to practical real-world business problems using commercial data mining software. As an applied course, the emphasis is on application and interpretation of various data mining methods using business cases and data.
BI 5400 Generative AI for Businesses
Prerequisite: BI 5100, MBA 5220
In this course, graduate Business Intelligence students explore core concepts and applications of Generative AI in business. They learn the basics of the technology, assess its impact on business operations, and evaluate its advantages and limitations. Students discover how Generative AI can enhance business strategies and, through hands-on exercises, build models to apply Generative AI in real-world business scenarios.
BI 5450 Data Governance, Security and Ethics
Prerequisite: BI 5060, BI 5100, MBA 5220
In this course, students explore critical areas of data governance, security, and ethics essential for modern organizations. They learn the importance of responsible data management, covering core principles of data governance. Key topics include data security and privacy, ethical considerations in data use, and practical applications of data ethics. The course also addresses data ownership, rights, and emerging issues. Students develop skills to effectively communicate best data practices within their organizations, fostering a culture of data integrity and security.
BI 5900 Integrated Business Intelligence Projects
Prerequisite: BI 5100, MBA 5220, MBA 5230
In the Integrated Business Intelligence Capstone, students engage in a project-based experience, applying business intelligence techniques to solve complex, real-world business challenges. The course begins with a review of essential BI concepts and progresses to hands-on dataset preparation for analysis. Students first tackle individual analyses of complex business problems before moving into team-based projects. The course culminates in a professional presentation of findings to a panel of academic and industry experts.
Flexible by Design
Designed for working professionals and driven students alike, MSU Denver’s MSBI program offers flexible learning formats that adapt to your schedule. Whether you prefer in-person connection or remote access, you can move forward without stepping away from your career.
SyncFlex
Instructional activities are facilitated in-person and online using live virtual meetings technology. Students have the flexibility to choose between in-person and live virtual instruction each class session.
Hybrid In-Person
The course will be fully online and asynchronous. Quizzes, reading assignments, and written assignments will all be completed online. However, all exams must be proctored in-person — typically on campus at the Testing Center.
Featured Faculty
Joseph Hasley
Dr. Joseph Hasley completed his Doctorate degree in Information Systems at the University of Colorado Denver, and additional graduate work in Management Information Systems at the University of Iowa. He regularly teaches courses in Business Analytics, Telecommunication Systems, and Python-based problem solving. Dr. Hasley leads the College of Business’ Assurance of Learning efforts. His research has been recently published in the American Journal of Management, the Journal of Ethical and Legal Issues, and in Information Systems and e-Business Management.
Viktor Kiss
Dr. Viktor Kiss completed his Doctorate in Economics / Business Administration at the University of Pecs in Hungary; he has a graduate degree from the Middlesex University in London and is an affiliate member of the Massachusetts Institute of Technology alumni association thorough completing two graduate level programs offered by the institution.
He teaches quantitative courses, ranging from Business Analytics through Machine Learning to more advanced Data Mining courses. His research focuses on the area of modeling complex systems. Recent work has been published in Renewable and Sustainable Energy Review, Ecological Economics, Energy, Ecological Indicators and the American Journal of Management.
Brian Lambert
Dr. Brian Lambert completed his Doctorate degree in Operations Research at the Colorado School of Mines, and additional graduate work in Operations Research at the Naval Postgraduate School in Monterey California. Prior to joining the Department of Computer Information Systems and Business Analytics at MSU Denver, he worked as a Senior Operations Research Analyst for Boeing Corporation in Denver Colorado, and as a Senior Consultant for Kromite, LLC. His current research interests focus on Applications of Data Envelopment Analysis. He recently published in the Journal of Applied Business and Economics. He regularly teaches Descriptive, Predictive and Prescriptive Analytics courses as well as Python-based Problem-Solving courses.
Edgar Maldonado
Dr. Edgar Maldonado completed his Doctorate in Information Sciences and Technology at the Pennsylvania State University; he completed graduate work in Business Analytics at the Georgia Institute of Technology. Dr. Maldonado has experience as a software support engineer for banking networks and recently did extensive consulting IT work in the greater Denver metro area. His research work has been published in various journals including the Journal of the Association for Information Systems, Journal of Ethical and Legal Issues, Journal of Higher Education Theory and in the American Journal of Management. He regularly teaches the integrative course Systems Analysis and Design, as well as several other Information Systems and Business Analytics courses.
Scott Margolis
Scott Margolis is an executive with over 20 years of leadership experience in data governance, privacy, cybersecurity, and risk management, advising Fortune 100 organizations. He holds an MBA in Data Analytics from the University of South Florida and teaches Business Intelligence and Data Analytics at MSU Denver. His teaching bridges data science, regulatory frameworks, business intelligence, and emerging technologies, such as AI. Professor Margolis holds two U.S. patents for international data compliance systems and copyrights for enterprise risk software. He has published thought leadership on privacy automation, AI governance, and global regulatory obligations. He serves on the Cybersecurity Advisory Boards at MSU Denver and the University of Tampa and is certified as a Fellow of Information Privacy (FIP), CIPP/US, and CIPT from the International Association of Privacy Professionals, and holds the PMP credential from the Project Management Institute. Professor Margolis received his undergraduate degree in Computer and Management Science from the Metropolitan State College of Denver.
Abel Moreno
Dr. Abel Moreno completed his Doctorate degree in Engineering at North Dakota State University and additional graduate work in Industrial Engineering and Management also at North Dakota State University. He currently serves as Chair of the Computer Information Systems and Business Analytics department. He regularly teaches Business Analytics courses. His research work has been published in Managerial and Decision Economics, International Journal of Production Research, International Journal of Flexible Manufacturing Systems, Industrial Marketing Management, and more recently in the American Journal of Management, and the Journal of Marketing Development and Competitiveness.
Ugur Sener
Dr. Ugur Sener holds a Ph.D. in Business Management from Istanbul Aydin University, an MBA from Beykent University, a B.S. in Industrial Engineering from METU in Ankara. Dr. Sener spent nine years in the steel industry, where he worked in sales engineering and business development roles at Borusan Mannesmann. Dr. Sener’s research focuses on hybrid forecasting, machine learning, and deep learning. His work integrates traditional statistical approaches—such as ARIMA and regression models—with modern artificial intelligence techniques including neural networks and deep learning architectures to improve predictive performance in economic and business contexts. His recent publications appear in peer-reviewed journals such as Computational Economics, Review of Economics, Sage Open and Cogent Economics & Finance. Dr. Sener regularly teaches courses in Business Analytics and Data Science.
Benjamin Flebbe
Ben Flebbe completed his graduate work at the University of Connecticut. He served as a senior actuarial analyst at Humana and a financial analyst at TransAmerica. In those roles he was responsible for Medicare Advantage filings, properly rating cost saving measures, rating reinsurance, optimizing workflows, and reserving retirement products. His focus now in academia is on pedagogy. He also currently serves as a consultant to Trailcrest Capital and is a day trader. He teaches Business Analytics courses.
Department Contact Information
Admin Building Suite 590
303-615-0660
Address:
Dept. of CIS & Business Analytics
Metropolitan State University of Denver
Campus Box 45
P.O. Box 173362
Denver, CO 80217-3362