1. Introduction
1.1 Overview
1.1.1 The evolution of data analytics: From traditional BI to AI-driven insights
1.1.2 Key technologies shaping the future of data analytics (AI, machine learning, and automation)
1.2 Business Applications
1.2.1 Use cases of AI in predictive analytics, customer insights, and operational optimization
1.3 Data-Driven Decision-Making in the AI Era
1.3.1 How AI-enhanced analytics is transforming business strategy and operations

2. AI and Machine Learning in Data Analytics
2.1 Foundations of AI and Machine Learning in Data Analysis
2.1.1 How machine learning models work in the context of data analytics
2.1.2 Supervised vs. unsupervised learning for business insights
2.2 Automated Data Processing and Pattern Recognition
2.2.1 AI-powered data cleaning, transformation, and analysis automation
2.2.2 Identifying trends and patterns in large datasets using AI
2.3 Hands-On Lab: Applying machine learning algorithms to analyze business data

3. Predictive Analytics and Forecasting
3.1 Introduction to Predictive Analytics
3.1.1 Using historical data to predict future outcomes
3.1.2 Predictive modeling techniques: Regression, classification, and time-series analysis
3.2 AI for Demand Forecasting and Customer Behavior Prediction
3.2.1 How AI predicts customer behavior and product demand
3.2.2 Real-world applications of AI in sales forecasting and inventory management
3.3 Hands-On Lab: Building a predictive model to forecast business trends

4. Data Visualization and Storytelling
4.1 Advanced Data Visualization Techniques(Ref: Building Decentralized Applications (dApps) with Blockchain Technology)
4.1.1 Creating compelling data visualizations with AI-driven insights
4.1.2 AI-powered tools for real-time data visualization and reporting
4.2 The Role of AI in Data Storytelling
4.2.1 Turning raw data into narratives that drive business decisions
4.2.2 Enhancing business presentations with AI-generated visualizations
4.3 Hands-On Lab: Creating AI-enhanced dashboards and reports using a data visualization tool

5. Real-Time Analytics and Automation
5.1 Real-Time Data Processing
5.1.1 Implementing real-time analytics for instant decision-making
5.1.2 AI’s role in enabling continuous monitoring and analysis
5.2 Automating Business Processes with AI and Data
5.2.1 Leveraging AI to automate decision-making based on data insights
5.2.2 Case studies of businesses using real-time analytics to drive growth
5.3 Hands-On Lab: Setting up real-time data processing for an operational dashboard

6. Ethical Considerations and Future Trends
6.1 Ethics and Bias in AI-Driven Analytics
6.1.1 Addressing data bias and ensuring fairness in AI models
6.1.2 Ethical concerns around data privacy and AI in business
6.2 Future Trends
6.2.1 AI and machine learning advancements shaping the future of analytics
6.2.2 The convergence of AI, IoT, and edge computing in data analytics
6.3 Final Project: Developing an AI-driven data analytics strategy for a business scenario

This training will provide participants with the theoretical knowledge and practical skills needed to implement data analytics driven by AI in their organizations, enabling them to harness the power of data for smarter decision-making and business growth.