Professional Certificate in Business Analytics and Applied AI

Curriculum

• Understand how AI and machine learning are reshaping team workflows, roles, and daily operations — and learn practical leadership approaches to support team adaptation and maintain performance through change
• Explore strategies for building AI literacy, addressing resistance, and motivating teams through the adoption of new tools and ways of working using proven frameworks including Kolb's Learning Styles and Tuckman's team development model

• Learn how to evaluate AI opportunities at an organizational level, identify business problems suited to AI solutions, and build the cases needed to gain alignment across leadership and frontline teams
• Develop structured approaches for planning and implementing AI initiatives, overcoming bias and resistance, and scaling AI across teams with sustainable governance and continuous improvement frameworks

• Understand how machine learning methods — including clustering, classification, neural networks, and generative AI — produce outputs, and develop the judgment to evaluate when those outputs are trustworthy and actionable
• Build practical skills for interpreting, communicating, and commissioning analytics work — including how to use prompt engineering effectively and apply data-driven insights responsibly across business functions

• Learn to evaluate AI-assisted analytics, create and interpret data visualizations using Excel and Power BI, and apply techniques including regression, customer segmentation, A/B testing, and binary outcome prediction to real business scenarios
• Develop the ability to distinguish meaningful insights from misleading results and communicate analytical findings confidently across marketing, operations, finance, and customer strategy functions

What You'll Learn

• Can identify AI opportunities across business functions and evaluate when AI tools and analytical approaches are appropriate — and when they are not.
• Apply practical leadership frameworks for leading teams through AI-driven change, supporting workforce adaptation, and building the organizational alignment needed for successful AI adoption.
• Understand how to interpret machine learning outputs — including clustering, regression, classification, and generative AI — critically and responsibly, distinguishing meaningful insights from misleading results.
• Can evaluate AI-assisted analytics using tools such as Excel and Power BI, applying techniques including data visualization, customer segmentation, regression modeling, A/B testing, and binary outcome prediction to real business scenarios.
• Apply structured approaches for communicating data-driven insights to non-technical stakeholders and commissioning analytics work from technical teams with clarity and confidence.
• Understand how responsible AI principles, ethical considerations, and governance frameworks apply to data-driven decision-making across marketing, operations, finance, and customer strategy functions.