MS-AIB academics

Explores modern artificial intelligence (AI) technology, applications, techniques and their implications for business. People, process and technology factors related to the innovation and adoption of AI in the commercial enterprise and how it shapes the competition and society in the future. Also covers implications for information systems professionals as it pertains to managing the AI infrastructure (robots, algorithms, platforms).

Provides a foundation in programming fundamentals, the skills to combine and manipulate structured and unstructured data, and the ability to summarize, visualize, and draw insights from the data. This course may be waived with demonstrated Python proficiency.

Ensuring the foundational understanding of contextualized analytics within the business enterprise continuum by covering how data flows and is managed across the landscape of enterprise business processes.

Addresses central issues in managing information to achieve competitive advantage and support innovation. Specific topics covered include data quality, data modeling using both entity relationship models and star schemas, data governance, document management, document search, enterprise performance management and visualization techniques.

Charting a roadmap for data-driven decision making and getting a practical understanding of how IT tools and techniques can allow managers to extract predictive analytics and patterns from primarily numeric data.

The focus of this course is on three primary areas: gaining a thorough understanding of business processes, investigating the enterprise systems that underpin them, and exploring how digital transformation, with a special emphasis on emerging AI technologies, can significantly improve business results.

Students will benefit from understanding practical ramifications of Information and Information Technology governance decisions, how they support broader business objectives, and implications of geopolitical differences. Business students will gain insights into best practices of governing data analytics, artificial intelligence/advanced algorithms (AI), cloud platforms, information and cybersecurity, as well as understand their pivotal role in supporting global business strategy.

Deep learning applications have become an integral part of our lives over the last decade. Applications based on deep learning models cover a wide spectrum of industries including retail, automotive, manufacturing, health care, banking, insurance, agriculture, security and surveillance. Hands-on look at the latest models, trends, challenges and strategy of deep learning applications in business.

Uses tools and techniques to analyze unstructured data that are applied to business problems to support informed decision making and the extraction of predictive analytics and patterns from primarily nonnumeric data.

Delves into the strategic, technical and ethical dimensions of leveraging AI and analytics to transform businesses. Focuses on formulating strategies that integrate AI capabilities to sustain a competitive advantage.