Data Analytics with Microsoft Generative and Agentic AI Bootcamp

Course Description

This SkillDynamo course develops practical, role-relevant capability in building data-analytics solutions with Microsoft generative and agentic AI across preparation, semantic analysis, copilots, automation, orchestration, evaluation, governance, and deployment. The learning path progresses from essential concepts and planning to implementation, validation, troubleshooting, and operational use.

Learners work through realistic scenarios, guided demonstrations, applied exercises, and evidence-based reviews. Every topic is connected to workplace responsibilities, common risks, quality controls, and measurable outcomes.

The course helps professionals make informed decisions, identify failure points, communicate results, and create an actionable improvement plan for future projects.

What You Will Learn

• Design an analytics architecture using Microsoft data, AI, Power Platform, Fabric, Azure, and approved integration services
• Prepare governed data, semantic definitions, metadata, access, quality, lineage, retrieval, and analytical context
• Build prompts, structured outputs, grounded retrieval, summarization, classification, explanation, and insight-generation patterns
• Create analytical copilots that use enterprise context, citations, validation, human review, and feedback loops
• Design agents with goals, instructions, memory, tools, actions, approvals, fallback, escalation, and multi-step orchestration
• Integrate agents with data sources, APIs, Power Automate, applications, monitoring, and controlled business workflows
• Evaluate accuracy, relevance, grounding, robustness, latency, cost, safety, bias, privacy, and operational impact
• Apply identity, networking, secrets, permissions, responsible-AI controls, deployment pipelines, telemetry, incidents, and improvement

SkillDynamo Training Approach

SkillDynamo delivers this course through demonstrations, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, solution reviews, and a final improvement plan. Learners repeatedly practice planning, implementation, evidence collection, testing, correction, and communication so the skills transfer confidently into real workplace situations.