Master 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
• Explore large language models, embeddings, and prompt engineering from a decision-maker's perspective, and build practical skills for interpreting, commissioning, and communicating analytics and AI work across business functions
• Examine the full analytics lifecycle — from data foundations and preprocessing through unsupervised and supervised learning, generative AI infrastructure, and critical interpretation — developing the end-to-end fluency needed to lead data-driven organizations

• 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
• Explore hands-on applications of clustering, simple and multiple regression, percentage change models, and logistic regression — developing the ability to select the right technique, interpret outputs accurately, and recognize common analytical pitfalls
• 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 — with the depth and rigor expected of a master-level analytics credential.
• Apply practical leadership frameworks for leading teams and organizations through AI-driven change, supporting workforce adaptation, and building the strategic alignment needed to scale AI initiatives sustainably.
• Understand the full analytics lifecycle — from data foundations, preprocessing, and storage through unsupervised learning, supervised learning, neural networks, generative AI, and critical interpretation — and apply that knowledge to evaluate and commission data-driven work.
• Can interpret machine learning outputs — including clustering, regression, classification, neural networks, and large language models — critically and responsibly, with the ability to assess model performance, recognize limitations, and communicate findings to diverse stakeholders.
• Apply hands-on analytics skills using Excel, Power BI, and AI-assisted tools across real business scenarios in marketing, operations, finance, and customer analytics — selecting the right technique, interpreting outputs accurately, and avoiding common analytical pitfalls.
• Understand how prompt engineering, embeddings, and generative AI outputs can be leveraged for business decision-making, and how to integrate qualitative and quantitative methods to extract meaningful insight.
• Can evaluate the ethical, governance, and responsible AI considerations involved in data-driven decision-making and apply frameworks for accountable, transparent, and sustainable use of AI across organizational functions.