Day 1

Session 1: Introduction to AI and Business Intelligence

• What is AI and how does it enhance business intelligence?
• Key concepts in BI: data warehousing, dashboards, and reporting
• The evolution of data-driven decision-making with AI

Session 2: AI Techniques for Business Insights

• Overview of machine learning in BI: classification, regression, clustering
• Introduction to predictive analytics for forecasting trends
• Natural language processing (NLP) for sentiment analysis and text mining

Session 3: Hands-on Lab: Applying AI for Business Insights

• Importing and exploring business datasets using Python and BI tools
• Implementing basic machine learning models for sales prediction
• Visualizing data trends with interactive dashboards

Session 4: Predictive Analytics in Business Decision-Making

• Building predictive models for customer behavior analysis
• Understanding key performance indicators (KPIs) and business metrics
• Case study: Predicting customer churn in a subscription-based business

Session 5: Hands-on Lab: Building Predictive Models

• Using Scikit-learn for predictive analytics
• Evaluating model performance using accuracy, precision, and recall
• Applying predictive insights to real-world business scenarios

Day 2

Session 1: Data Visualization and AI-Powered Dashboards

• Importance of data visualization in strategic decision-making
• Creating interactive dashboards with tools like Power BI or Tableau
• Integrating AI models into BI dashboards for real-time insights

Session 2: Hands-on Lab: Data Visualization with Business Intelligence Tools

• Designing dashboards to display AI-driven insights
• Creating visualizations for trend analysis and KPI monitoring
• Practical exercise: Building a BI dashboard for sales performance tracking

Session 3: Real-Time Analytics and AI in Business Intelligence

• Introduction to real-time data processing with AI
• Case study: AI-powered fraud detection systems in financial services
• Using AI for real-time marketing analytics and customer segmentation

Session 4: Hands-on Lab: Implementing Real-Time Analytics

• Setting up real-time data pipelines with AI integration
• Monitoring business metrics in real-time dashboards
• Automating alerts and decision-making processes with AI

Session 5: Ethical Considerations and AI Governance in BI

• Addressing data privacy, security, and compliance in AI-driven BI
• Ethical challenges: bias in AI models and decision-making transparency
• Best practices for responsible AI implementation in business environments

Session 6: Group Activity and Course Wrap-Up

• Group project: Apply AI techniques to a business case study for decision-making
• Presenting AI-driven insights and strategic recommendations
• Key takeaways and resources for continued learning in AI and BI
• Q&A session to address participants’ specific questions