Overview

The Modern Data Strategy for Enterprise Generative AI program offers a comprehensive journey along three courses—Data Frameworks for Gen AI, Advanced Data Techniques for Enterprise AI Systems, and Data Lineage & Ethical Frameworks for Responsible AI. This specialized program is designed to equip professionals with the right skills and knowledge with respect to modern data strategies, security and governance, and scalable AI systems. Learners will explore structured and unstructured data, metadata tagging, vector databases, unified data architectures, and responsible AI governance through hands-on labs, case studies, and expert-led sessions. With industry expert David Drummond leading the curriculum, the program manages real-world applications and enterprise needs.

Syllabus

Course 1: Data Frameworks for Generative AI
Course 2: Advanced Data Techniques for Enterprise AI Systems
Course 3: Data Lineage & Ethical Frameworks for Responsible AI

Courses

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5 hours 53 minutes
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Modern GenAI (LLMs, RAG, agentic AI) succeeds or fails on the quality, structure, and governance of the data behind it. In this course, you’ll learn how structured and unstructured data drive GenAI applications, and how to design comprehensive data frameworks, taxonomies, and governance practices that reduce hallucinations, improve relevance, and make AI outcomes reliable. You’ll examine LLM limitations, connect them to data quality and metadata strategy, and implement taxonomy led architectures that future proof enterprise AI. Through case studies, practice assignments, and guided dialogues, you’ll develop the skills to design, validate, and operationalize GenAI ready data foundations for real products and platforms. By the end, you’ll be able to create enterprise grade data frameworks that deliver consistent, ethical, and high performing results.
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16 hours 55 minutes
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Generative AI succeeds or fails on the quality of your data strategy. In this hands on course, you’ll learn how to design scalable data frameworks and governance models that power LLMs, RAG, and agentic AI with reliable, ethical, and context rich information. The curriculum covers modern data strategy fundamentals, taxonomy design, and responsible AI practices—equipping you to reduce hallucinations, enforce compliance, and accelerate delivery of production ready AI solutions. Through case studies, interactive dialogues, labs, and practice assignments, you’ll apply taxonomies, metadata, and data quality controls to real world scenarios. By the end, you’ll be able to architect enterprise data foundations that make GenAI robust, explainable, and future proof.
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7 hours 54 minutes
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Transform how your organization builds trust in AI. Learn to design end‑to‑end data lineage and ethical governance frameworks that make AI explainable, auditable, and compliant—without slowing innovation. In this hands‑on course, you’ll capture provenance from source to model, manage metadata and documentation (datasheets, model cards), and operationalize risk controls aligned to industry guidance. Practice integrating lineage with the AI lifecycle—ingestion, training, deployment, monitoring—and implement privacy, fairness, and quality assurance guardrails. You’ll produce audit‑ready evidence packs, dashboards, and review artifacts that withstand scrutiny from leadership, regulators, and clients. By the end, you’ll design governed AI workflows, evaluate risks and mitigations, and institutionalize ethical review gates for reliable, accountable outcomes.