Course Syllabus

Complete curriculum overview for the AI Analyst certification program

Course Overview

20h Total Content
16 Modules
8 Hands-on Labs
Self-paced Format
Prerequisites: None - designed for business professionals with no coding experience

Learning Objectives

By the end of this course, you will be able to:

  • Understand AI economics and select appropriate models for different use cases
  • Write effective prompts that consistently produce desired outputs
  • Design AI-augmented workflows with proper quality controls
  • Implement production-ready AI assistants with context management
  • Build compelling business cases for AI adoption
  • Develop governance frameworks for responsible AI use

Curriculum

Phase 1

AI Literacy

5 hours

Build foundational understanding of how AI systems work and how to communicate with them effectively.

Modules

1.1 Economics of Intelligence 45 min
Token economicsCost hierarchiesModel selection
1.2 Context and Memory 45 min
Context windowsMemory limitationsConversation design
1.3 Providers and Models 45 min
OpenAI, Anthropic, GoogleModel capabilitiesUse case matching
1.4 Prompting as Management 45 min
Clear instructionsPersona designOutput formatting

Labs

Persona Stress Test 1 hour
Chain of Thought Audit 1 hour

Phase Deliverable

Prompt Library - A collection of tested, effective prompts for common tasks

Phase 2

Workflow Engineering

5 hours

Learn to design AI-augmented workflows with appropriate quality controls and human oversight.

Modules

2.1 Workflow Decomposition 45 min
Task analysisAI suitabilityWorkflow mapping
2.2 Quality Gates 45 min
Validation pointsError detectionHuman review
2.3 Human-AI Collaboration 45 min
Role boundariesEscalation pathsFeedback loops
2.4 Batch Processing 45 min
Parallel executionRate limitingError handling

Labs

Workflow Design Challenge 1.5 hours
Quality Gate Implementation 1 hour

Phase Deliverable

Workflow Diagram - A complete AI-augmented workflow for a real business process

Phase 3

Agentic Orchestration

7 hours

Design and deploy autonomous AI agents, multi-agent systems, and intelligent workflows that reason and act.

Modules

3.1 No-Code AI Tools 45 min
Tool landscapeSelection criteriaEvaluation
3.2 API Fundamentals 45 min
REST APIsAuthenticationWebhooks
3.3 Automation Platforms 45 min
ZapierMaken8n
3.4 Testing and Deployment 45 min
Testing strategiesMonitoringScaling
3.5 Customization & Fine-Tuning 45 min
RAG vs Fine-tuningEconomicsWhen to customize

Labs

Build an AI Assistant 1 hour
Multi-Agent Orchestration 1.5 hours

Phase Deliverable

Multi-Agent System - A working system where AI agents collaborate to complete complex tasks

Phase 4

Strategy & Economics

5 hours

Master the business side of AI implementation including ROI analysis and organizational adoption.

Modules

4.1 ROI Analysis 45 min
Cost modelingValue measurementBreak-even analysis
4.2 Risk Management 45 min
AI limitationsFailure modesMitigation strategies
4.3 Governance Frameworks 45 min
PoliciesComplianceEthics
4.4 Organizational Adoption 45 min
Change managementTrainingCulture

Labs

Business Case Development 1.5 hours
Governance Policy Draft 1 hour

Phase Deliverable

AI Strategy Document - A comprehensive plan for AI adoption in an organization

Capstone

Capstone Project

4-6 hours

Apply everything you've learned by designing and implementing a complete AI solution for a real business problem.

Project Components

  • Problem analysis and AI suitability assessment
  • Workflow design with quality gates
  • Working prototype or detailed specification
  • ROI projection and risk assessment
  • Implementation roadmap

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