Module 1: AI & Analytics Foundations
- Analytics Maturity Spectrum
- Descriptive to Prescriptive Analytics
- AI Positioning in Analytics Workflow
- Core AI Concepts
- Machine Learning, Deep Learning, Generative AI
- Types and Applications of AI Models
- AI Use Cases
- Industry Applications: Finance, Retail, Healthcare, Logistics
- Real-World AI Analytics Examples
- AI Ethics & Governance
- Bias and Fairness in AI Models
- Responsible Data Use Principles
Module 2: Prompt Engineering for Analytics
- Prompt Engineering Principles
- Structure of an Effective Prompt
- Common Prompting Patterns
- AI Tool Landscape
- Claude, ChatGPT, and Gemini for Analytics
- Tool Selection by Task Type
- Analytical Prompt Design
- Prompting for Data Cleaning Tasks
- Prompting for Model Generation
- Prompt Refinement Techniques
- Iterative Prompt Improvement
- Error Interpretation and Correction
Module 3: Data Foundations for AI
- Data Quality Dimensions
- Completeness, Accuracy, Consistency
- Impact of Quality on AI Outputs
- Google Colab Environment
- Navigating the Colab Interface
- Running and Managing Notebook Cells
- AI-Assisted Data Wrangling
- Prompting for Pandas Transformations
- Handling Missing Values and Duplicates
- Cross-Tool Validation
- Power Query vs. AI-Generated Python
- Result Verification and Interpretation
Module 4: Machine Learning for Business Analytics
- Machine Learning Concepts
- Supervised vs. Unsupervised Learning
- Regression, Classification, Clustering
- AI-Generated Model Code
- Describing Business Problems to AI
- Running Scikit-learn Models in Colab
- Model Evaluation Metrics
- Accuracy, Precision, Recall, AUC
- Business Interpretation of Metrics
- Model Reliability
- Overfitting and Underfitting
- Data Leakage and Validation Splits
Module 5: AI Features in Power BI
- Power BI AI Visuals
- Key Influencers Visual
- Q&A and Smart Narratives
- Forecasting & Anomaly Detection
- Built-in Forecasting in Line Charts
- Anomaly Detection Configuration
- AI Builder & AutoML
- No-Code ML Inside Power BI
- Model Integration with Reports
- Copilot for Power BI
- Generative AI Report Assistance
- Excel Copilot for Analytics Tasks
Module 6: Data Visualization with AI
- Visualization Principles
- Choosing the Right Chart Type
- Visual Hierarchy and Clarity
- AI-Generated Chart Code
- Prompting for Matplotlib and Seaborn
- Running Visualizations in Colab
- NLP & Text Analytics
- Sentiment Analysis with AI Scripts
- Text Mining for Business Data
- Data Storytelling
- Structuring Insights for Executives
- Narrative-Driven Report Design
Module 7: Integrating Colab with Power BI
- Python Visual in Power BI
- Enabling Python Scripts in Power BI
- Connecting Colab Outputs to Reports
- End-to-End Workflow Design
- Data Flow: Colab to Power BI
- Combined Dashboard Architecture
- AI Governance in BI
- Data Privacy in AI Pipelines
- Model Accountability Principles
- Workflow Optimization
- Reusable AI Prompting Templates
- Efficiency Patterns in Analytics Projects
Module 8: Applied AI Analytics Project
- Capstone Project Framework
- Business Problem Scoping
- Dataset Selection and Preparation
- Full Pipeline Execution
- Data Cleaning, Modeling, Visualization
- AI Tool Integration Across Stages
- Insight Presentation
- Communicating Findings to Stakeholders
- Non-Technical Audience Considerations
- Certification Preparation
- Key Concept Review Across Modules
- Exam Strategy and Format Overview
