Curriculum: Artificial Intelligence in Business Graduate Certificate

Next Term Application Deadline

04.01.2025

Total Credits

Next Term Starts

05.12.2025

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*This program is now enrolling for Summer 2025.

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Curriculum at a Glance

The Lindner College of Business curriculum for the AI in Business graduate certificate includes four core courses (totaling eight credits) and two elective courses (totaling four credits).

CURRICULUM AT A GLANCE

Core Courses Electives

Course	Title / Description	Credit
IS 7065	Generative Artificial Intelligence for Business Course: IS 7065 Credit: 2 This course examines the technology underlying modern generative artificial intelligence / machine learning models from a business perspective, including their uses in coding, professional and artistic applications, and the various controversies and challenges to work and/or society they may pose.	2
BANA 7075	Machine Learning Design for Business Course: BANA 7075 Credit: 2 This course provides a framework for developing real-world machine learning systems that are deployable, reliable, and scalable. Designing machine learning systems is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified business requirements. Without deliberate design, machine learning systems can get outdated quickly because (1) the tools continue to evolve, (2) business requirements change, and (3) data distributions constantly shift. Students will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, and how to continually monitor and deploy changes to ML systems for successful business applications. They will also be exposed to managing the human side of ML projects such as team structure and business metrics.	2
IS 7085	Governance of AI/ML Systems Course: IS 7085 Credit: 2 This course teaches students how to develop, scale-up, and sustainably manage high-performing Artificial Intelligence/Machine Learning systems in business organizations. It introduces concepts and techniques that enable the development of surrogate approaches to explain AI/ML models, build redundancy in AI/ML systems, and calculate and minimize risk of failures while using such approaches.	2
IS 8036	Survey of Machine Learning and Artificial Intelligence Course: IS 8036 Credit: 2 This course is a survey of Machine Learning (ML) and Artificial Intelligence (AI) from the Data Scientist’s perspective. It explores ML and AI topics, current and emerging technologies, and applications for students to gain understanding of the successful implementation of ML and AI to address key business and industry problems.	2

Course	Title / Description	Credit
BANA 7025	Data Wrangling Course: BANA 7025 Credit: 2 This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.	2
BANA 7046	Data Mining I Course: BANA 7046 Credit: 2 This is a course in the statistical data mining with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Data Processing. Variable Selection for linear regression and generalized linear regression. Out-of-sample Cross Validation. Generalized Additive models. Nonparametric smoothing methods. Classification and Regression Tree. Neural Network. Monte Carlo Simulation.	2
BANA 7047	Data Mining II Course: BANA 7047 Credit: 2 This is a course in statistical data mining with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Missing Data Imputation, Bootstrapping, Boosting and Multiple Additive Regression Trees, Bayesian Trees, Support Vector Machine, Discriminant Analysis, Cluster Analysis, Factor Analysis, Principle Component Analysis.	2
BANA 8090	Special Topics in Business Analytics Course: BANA 8090 Credit: 1-4 This course is used to explore topics of current interest in the BANA domain, that do not fall within the scope of any of the regularly scheduled courses. By the nature of the course, specific topics covered will vary with each offering.	1-4
IS 7036	Advanced Business Intelligence Course: IS 7036 Credit: 2 This course is designed for in-depth learning of BI concepts, including advanced dimensional modeling, data mining techniques, web mining, text mining, and BI 2.0. Students will apply and integrate the business intelligence knowledge from IS 7034 to implement a set of BI tools, such as ERWin, Teradata Data Warehouse Miner, and OLAP. This course also includes a case study and term project.	2
IS 8034	Big Data Integration Course: IS 8034 Credit: 2 This course presents an overview of the principles of data integration, the fundamental basis for developing useful and flexible business intelligence platforms. Modern data integration needs differ from traditional approaches in four main dimensions that parallel differences between big data and traditional data: volume, velocity, variety, and veracity.	2
IS 8070	Special Topics in IS Course: IS 8070 Credit: 1 This course is used to explore topics of current interest in the IS domain, that do not fall withinthe scope of any of the regularly scheduled courses. By the very nature of the course, specific topics covered will vary with each offering.	1

FAQs

How can this certificate benefit my career?

The AI in Business certificate enhances your skills in leveraging AI for business productivity and problem-solving, making you a valuable asset in a rapidly growing field. You will gain hands-on experience with the latest AI tools, which are highly sought after in today’s job market. Acquiring expertise in AI will position you as a valuable asset to your current or future employers and can lead to career advancement opportunities. The curriculum is designed by experts who are actively working on and researching the latest business applications of AI, ensuring that you are learning the most up-to-date and relevant skills to stay competitive in the job market.

As a student, you will gain access to Lindner’s extensive alumni network of 50,000 professionals, which can provide valuable connections, mentorship, and job placement assistance in this rapidly growing field.

Credits earned in the AI in Business certificate program can be applied towards a Master of Science in Information Systems or a Master of Science in Business Analytics degree. They may also be applied to UC’s MBA, providing a streamlined pathway to further your education and career prospects.
Do I need prior experience with AI tools to enroll?

No prior experience with AI tools is required. However, a basic background in Python programming and information systems is helpful.
How are the classes structured/what is “asynchronous?”

What is asynchronous online learning? Fully asynchronous courses allow you flexibility in your schedule. Once enrolled in a program, you may view instructional materials when it works best for you as long as you meet course deadlines. There is no live video lecture component for this type of learning. For more information on asynchronous learning visit our blog post: Synchronous and Asynchronous Learning

Once enrolled, you’ll use tools like Canvas and meet with your Student Success Coordinator on a regular basis to ensure you’re on track to graduate. Learn more about the UC Online experience in the video below.