Generative AI for Business Intelligence (BI) Analysts Specialization Course Syllabus

This specialization teaches business intelligence analysts how to leverage generative AI and large language models to accelerate insights, automate reporting, and transform decision-making. You'll master GPT-powered SQL generation, AI-augmented visualization, predictive storytelling, and ethical AI governance. Across 5 modules and a capstone project, you'll build practical skills to deliver data-driven narratives faster than stakeholders can ask for them. Total time commitment: 110 hours over 13 weeks.

Module 1: Foundations of Generative AI for BI Analysts

Introduction to large language models, their capabilities and limitations in business contexts. Learn how transformer architectures power ChatGPT, Claude, and enterprise LLMs. Understand token limits, hallucinations, and prompt engineering fundamentals. Review SQL and Python basics for BI professionals, and the data pipeline from warehouse to insights.

• How LLMs work: tokens, embeddings, and inference
• ChatGPT, Claude, Gemini, and open-source alternatives for business use
• Prompt engineering: instructions, context, and few-shot examples
• SQL and Python refresher for data analysts
• Responsible AI principles and bias recognition

Estimated time: 12 hours

Module 2: Natural Language Queries & Automated Reporting

Convert business questions into SQL and Python code using LLMs. Build automated report generators that translate natural language into executable queries. Implement self-service analytics where stakeholders ask questions in plain English. Learn techniques to validate AI-generated code and prevent SQL injection vulnerabilities.

• Prompting LLMs to generate SQL and Python efficiently
• Building natural language query interfaces
• Validating and testing AI-generated code
• Tableau Ask Data and Power BI Q&A deep dives
• Error handling and fallback strategies
• Automated insight extraction from query results

Estimated time: 18 hours

Module 3: AI-Enhanced Dashboarding & Interactive Visualization

Design and build interactive dashboards enhanced with AI-generated insights, descriptions, and anomaly alerts. Learn to generate chart recommendations from raw data. Implement dynamic narratives that adapt based on underlying metrics. Integrate ChatGPT APIs into Tableau and Power BI for real-time context generation.

• Dashboard design principles for AI-augmented insights
• Automated chart type selection and recommendations
• Generating human-readable insights from data
• Integrating OpenAI APIs with Tableau and Power BI
• Building interactive story layers with LLM narration
• Real-time anomaly detection and alerting

Estimated time: 16 hours

Module 4: Advanced Analytics: Predictive Storytelling & Sentiment Analysis

Master predictive storytelling techniques: creating narratives about what might happen next. Implement sentiment analysis at scale using transformers. Build time-series forecasting pipelines. Use LLMs to generate executive summaries and scenario planning narratives for business simulations.

• Predictive modeling and AI narrative generation
• Sentiment and topic analysis with transformer models
• Time-series forecasting enhanced with contextual language
• Scenario modeling and what-if analysis
• Generative business storytelling from statistical outputs
• Case studies: predictive BI in finance and supply chain

Estimated time: 20 hours

Module 5: Data Quality, Ethics & Governance for AI BI

Ensure responsible AI implementation in analytics. Use LLMs for automated data quality checks and anomaly detection. Establish governance frameworks, bias auditing, and compliance workflows. Build guardrails to prevent hallucinations and ensure data lineage transparency in AI-driven analytics.

• Data quality assessment with LLMs
• Bias detection in training data and model outputs
• Compliance and regulatory requirements (GDPR, SOX, HIPAA)
• AI model explainability and transparency
• Cost optimization and token efficiency
• Enterprise AI governance frameworks

Estimated time: 14 hours

Module 6: Implementation Strategy & Change Management

Build organizational adoption strategies for AI BI tools. Calculate ROI and business impact metrics. Plan change management initiatives and stakeholder communication. Address upskilling needs and create playbooks for rolling out AI-augmented analytics across teams.

• ROI calculation frameworks for AI BI investments
• Pilot program design and rollout timelines
• Stakeholder communication and training strategies
• Vendor evaluation (enterprise platforms vs. custom solutions)
• Building internal AI BI communities of practice
• Measuring adoption and success metrics

Estimated time: 12 hours

Module 7: Capstone Project

Design and implement a comprehensive AI BI transformation plan for a real or simulated organization. You'll assess current state analytics, identify high-impact use cases, design an implementation roadmap, and build a proof-of-concept dashboard or automated reporting system. Deliver an executive brief with ROI projections, risk mitigation, and governance recommendations.

• Organizational assessment and gap analysis
• AI BI roadmap with phased milestones
• Proof-of-concept implementation (dashboard, automated reports, or query interface)
• Cost-benefit analysis and ROI model
• Change management and governance plan
• Executive presentation and recommendations

Estimated time: 15 hours

Prerequisites

• 1+ years of experience in business intelligence, data analysis, or analytics
• Proficiency in SQL (SELECT, JOIN, WHERE clauses)
• Basic Python or similar programming language
• Familiarity with at least one BI tool (Tableau, Power BI, Looker, or similar)
• Understanding of business metrics and KPI definitions

What You'll Be Able to Do After

• Generate SQL and Python code using ChatGPT and other LLMs with validation workflows
• Build natural language query interfaces for self-service analytics
• Design AI-augmented dashboards that generate contextual insights automatically
• Implement predictive storytelling and sentiment analysis in business contexts
• Establish governance frameworks and mitigate bias in AI-driven analytics
• Calculate ROI and develop implementation strategies for AI BI adoption
• Lead organizational change management for AI analytics transformation