Courses and descriptions
MS in Business Analytics (MSBA) students admitted in Fall 2024 or later will take seven required core courses and select three elective* options to round out your 10 courses. Those admitted prior to Fall 2024 will take six core courses and choose four electives.*
*Effective Fall 2025, the core course, BAIS:6120 Analytics Experience, will not be required to earn the MSBA degree and will no longer be offered. Replace with a Business Analytics elective instead.
Business Analytics Certificate students will complete five core courses to earn the certificate.
Core courses
Data and Decisions (BAIS:9100 or MBA:8150)
This course introduces analytical techniques for making business decisions. Utilizing Excel, students apply descriptive and predictive analytical tools to solve practical business problems using real-world data. To deal with uncertainty in decision making, the course first introduces formal probability concepts and statistical methods for describing variability through topics such as decision trees, random variables, and hypothesis testing. Students then learn the practical application of techniques such as linear regression, Monte Carlo simulation, and linear optimization to model, explain, and predict for operational, tactical, and strategic decisions. (3 s.h.)
Advanced Analytics (BAIS:9110)
Development of data-driven, problem-solving skills for prediction of uncertain outcomes and prescription of business solutions; linear and nonlinear regression, Monte Carlo simulation, forecasting, data mining, and optimization utilizing spreadsheets and dedicated software packages. (3 s.h.) Prerequisite: BAIS:9100 or MBA:8150
Data Management (BAIS:6050)
Understanding how data is stored in databases and learning the tools used to access the data is key to creating datasets to answer many business questions; how to manage and access data in relational databases. (Formerly BAIS:9230 Database Systems) (3 s.h.)
Data Programming in Python (BAIS:6040)
Introduction to principles and practices of handling, cleaning, processing, and visualizing data using the Python programming language; basic data programming skills that can be applied to software development in any high-level programming language, data types, control structures, functions and modules, and other useful libraries for data manipulation and machine learning applications in Python. (3 s.h.)
Data Science (BAIS:6070)
Underlying concepts and practical computational skills of data mining tools including penalty-based variable selection (LASSO), logistic regression, regression and classification trees, clustering methods, principal components and partial least squares; analysis of text and network data; theory behind most useful data-mining tools and how to use these tools in real-world situations; software for analysis, exploration, and simplification of large high-dimensional data sets. (3 s.h.) Prerequisites: {BAIS:9100 or MBA:8150} and BAIS:6040
Visual Analytics (BAIS:6140) - Master's degree course
This course exposes students to the problems and challenges of effectively interpreting and communicating the pervasive data that surrounds us. It covers the area of information visualization, grounded in theoretical foundations of visual perception, cognition, information design, human-computer interaction, and the analysis of quantitative, unstructured and relational data. The course will follow a lecture/seminar style with discussion of assigned readings, critiquing of visualization examples, hands-on experience with commercial information visualization tools like Tableau, Power BI, and exploration of select open-source information visualization toolkits. (3 s.h.)
Analytics Experience (BAIS:6120) - Master's degree course
In this course, students will work in groups to complete semester-long projects pertaining to business analytics. All project stages are addressed including problem definition, data cleaning, analysis, and final presentation; appropriate tools from required courses used throughout. (3 s.h.) Prerequisites: {BAIS:9100 or MBA:8150}, BAIS:6050, BAIS:9110, BAIS:6040, and BAIS:6070
*Effective Fall 2025, this course (BAIS:6120) will not be required for the MSBA degree and will no longer be offered. Replace with a Business Analytics elective instead.
Elective courses
Agile Project Management (BAIS:9140)
This course prepares students to create or participate in a successful agile work environment. Students will learn various agile methods such as scrum, lean, Kanban, XP, as well as understand and apply tools, techniques, and approaches used in an agile setting. Students will understand how to apply advanced agile topics such as story mapping, advanced planning and estimating, and scaling methods. (3 s.h.)
Applied Deep Learning (BAIS:6250)
This course introduces students to deep learning and its practical applications. Students will learn key architectures such as convolutional neural networks, recurrent neural networks, and transformers. Students will explore their practical uses in natural language processing, computer vision, and time series forecasting, and gain hands-on experience with Python libraries like PyTorch. (3 s.h.) Prerequisites: {MBA:8150 or BAIS:9100}, BAIS:6040, and BAIS:6070
Applied Optimization (BAIS:6130)
This course will use optimization to make tactical, strategic decisions and to build machine-learning and AI models. Students will be introduced to advanced optimization skills including data collection and preparation, logical modeling, algorithm enhancements, solution interpretation and implementation, experience within a software environment, and applications in the various functional areas of business. (3 s.h.) Prerequisites: {MBA:8150 or BAIS:9100} and BAIS:6040
Cybersecurity (BAIS:6280)
High-level view of computer security and fostering a cybersecurity mindset which is in demand across all industries; frequent change of perspective from employee to CEO, casual home user, and hacker; broad range of topics; actionable items to make daily digital interactions more secure. (3 s.h.)
Data Leadership and Management (BAIS:6210)
Core chief information officer (CIO) basics; focus on how to keep technology, systems, and procedures supporting business goal outcomes including management of information technology (IT) teams, systems selection, vendor negotiation, change, information risk, data integrity, ethics, information system (IS) policies, strategies, cloud computing, and budget. (3 s.h.)
Generative AI (BAIS:6260)
This course offers an introduction to the basics of generative artificial intelligence (AI) models and their practical applications. Students will explore key concepts like modal representation and generative models, learning the scalability aspects of these models and the implementation techniques such as pretraining, finetuning, and prompt engineering. The course emphasizes hands-on skills, including how to work with pretrained models, evaluate performance, and ensure alignment with goals. Business case studies will help students understand how to apply AI responsibly and set realistic expectations for its use. Ethical issues, privacy concerns, and the regulatory landscape will also be discussed to prepare students for the challenges for using AI in real-world settings. (3 s.h.) Prerequisites: {MBA:8150 or BAIS:9100}, BAIS:6040, BAIS:6070, and BAIS:6250
Marketing Analytics (MKTG:9310)
Quantitative tools to support marketing planning decisions, including forecasting, elasticity analysis, conjoint analysis, and customer LTV; analysis of syndicated data. (3 s.h.) Prerequisite: BAIS:9100 or MBA:8150
Social Analytics (BAIS:6105)
Exploration of collection, management, and analysis of social data (interactions among actors); actors as individuals, organizations, or other collectives; sources for social data including social media, websites, annual reports, press releases, articles, and other traditional media. (3 s.h.) Prerequisites: BAIS:6040 and BAIS:6070
Text Analytics (BAIS:6100)
Concepts and techniques of text mining; the practice of using statistical tools to automatically extract meaning and patterns from collections of text documents. Topics include document representation, text classification and clustering, sentiment analysis, and topic modeling. (3 s.h.) Prerequisites: BAIS:6040 and BAIS:6070
Value Creation Using AI (BAIS:6240)
Comprehensive understanding of how artificial intelligence (AI) can be harnessed to create value in various business sectors including AI fundamentals; frameworks for value creation; competitive strategies using AI; critical success factors for AI-based projects; and the identification of impactful use cases for a given industry. Ethical considerations, privacy, trust, and security issues related to AI will also be addressed. (3 s.h.)
Check out the business elective options, delivered through the Iowa MBA Program. All MBA courses (except capstone classes) are available as long as prerequisites are met.