Curriculum: Artificial Intelligence in Business Graduate Certificate

Curriculum: Artificial Intelligence in Business Graduate Certificate
07.31.2024
12
08.26.2024
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curriculum icon 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).

Core Courses

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 Without deliberate design, machine learning systems can get outdated quickly because (1) the tools continue to evolve, (2) business requirements change, and (3) data distributions constantly shift.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
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Electives

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 statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include data preprocessing, k-nearest neighbors, generalized linear regression, subset and LASSO variable selection, model evaluation, cross validation, classification and regression trees.
2
BANA 7047
Data Mining II
Course: BANA 7047
Credit: 2
This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include advanced trees: bagging, random forests, boosting; nonparametric smoothing methods; generalized additive models; data preprocessing/scaling; neural networks; deep learning; cluster analysis; association rules.
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
Data Mining for Business Intelligence
Course: IS 7036
Credit: 2
This course is designed for the in-depth learning of data-mining knowledge and techniques in the context of business intelligence. The topics include association rules, classification, clustering and text mining. Students will apply and integrate the business intelligence knowledge learned in this course in leading software packages.
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 within the scope of any of the regularly scheduled courses. By the very nature of the course, specific topics covered will vary with each offering.
1
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