Master of Business Analytics and AI (MBAI)

The Master of Business Analytics and AI (MBAI) will give students insight in three main domains, which are applications of AI, Business Analytics and Management issues. Designed for graduates of Commerce or Business undergraduate degrees, this course-based professional Master program builds upon the core functional knowledge learned at the undergraduate level with a focus on advanced analytics and AI solutions to business problems.

The MBAI is a market-driven degree in an in-demand field at the intersection of technology and business. Graduates from this program will be equipped with the skills, knowledge and networks needed to succeed in today’s workplace as well as the ability to adapt to the ever-evolving workplace of the future. Students will be able to develop their strengths in digital economy, data analytics and artificial intelligence, all while building community and expanding connections with reputable industry partners.

What You’ll Learn

• Design and construct appropriate analytics solutions to solve business problems for data-driven decision making.
• Conceptualize and produce impactful presentations to communicate complex information to various stakeholders using data driven storytelling.
• Examine, adapt, and appraise machine learning and AI models for business problems using commercial and open-sourced technologies.
• Systematically examine implications of analytic models from multiple perspectives including identifying issues’ strategic value, business ethics, bias, privacy, trustworthiness, and fairness.
• Analyze data and use principles of database design implementation and administration.
• Critically examine data quality and combine multiple data sources and formats in preparation of ingesting data into analytics models.
• Create solutions using AI and analytics in new and existing business processes.
• Systematically appraise and contract the significance and reliability of competing models and analytics methodologies.
• Develop an understanding of the complexity of a complete data modelling project lifecycle from recognition and scoping to model maintenance.