Day 1: Introduction to AI and Data Fundamentals

• Overview of Artificial Intelligence and its role in data analytics

• Types and sources of data: structured, unstructured, big data

• Fundamentals of data quality, collection methods, and data governance

• Introduction to AI tools and platforms for data management

• Setting up data environments for AI analysis

Activities:

• Case studies on AI-driven data projects

• Practical exercise: Data sourcing and initial collection

Day 2: Intelligent Data Collection Techniques

• Traditional vs AI-enhanced data collection methods

• Data mining, web scraping, and sensor-based data acquisition

• Handling big data challenges with AI

• Data cleansing, validation, and preparation for analysis

• Tools and software for automated data collection

Activities:

• Hands-on with data collection tools

• Workshop: Data cleaning and preprocessing

Day 3: AI-Powered Data Analysis and Machine Learning Basics

• Introduction to machine learning concepts relevant to data analysis

• Descriptive, predictive, and prescriptive analytics

• AI algorithms for classification, clustering, and regression

• Using AI-powered software (e.g., Python libraries, Power BI AI features)

• Natural language processing and image data analysis overview

Activities:

• Implementing basic ML models on sample datasets

• Case study: Predictive analytics application

Day 4: Smart Reporting and Data Visualization

• Principles of effective reporting and storytelling with data

• Integrating AI insights into reports and dashboards

• Visualization tools: Tableau, Power BI, and AI-driven features

• Customizing reports for different stakeholders

• Automating report generation with AI

Activities:

• Design and build an interactive AI-powered report

• Data storytelling exercise

Day 5: Practical Applications, Ethics, and Future Trends

• Automating workflows and real-time AI reporting

• Enhancing decision-making with AI insights

• Data privacy, ethics, and regulatory considerations

• Measuring report effectiveness and continuous improvement

• Emerging AI trends in data analytics and reporting

Activities:

• Create a roadmap for AI integration in reporting

• Final project presentations and feedback