Course Prerequisites:
Basic Computer Skills: Familiarity with software applications.
Foundational Data Concepts: Basic knowledge of data analysis (beneficial, not mandatory).
Open to All: Suitable for all expertise levels, with an interest in AI, ML, and BI.
Modules:
Module 1: Introduction to AI and BI Fundamentals
1.1 Overview of AI and BI Integration
1.2 Core Concepts in Business Intelligence
1.3 Data Analysis Process and AI's Role
1.4 BI Trends and Challenges
1.5 Case Study
1.6 Hands-On Activity
Module 2: Python for AI-Driven Business Intelligence
2.1 Python Programming Fundamentals
2.2 Advanced Python Libraries for BI
2.3 Visualization with Python
2.4 Hands-On Activity
Module 3: Data Preparation and Feature Engineering with AI
3.1 Data Collection Techniques
3.2 Data Quality & Evaluation
3.3 Advanced Data Preparation
3.4 Hands-On Activity
Module 4: Machine Learning (ML) for Business Intelligence
4.1 ML Models for BI
4.2 Hands-On Activity
Module 5: Advanced AI and Generative AI for BI
5.1 Deep Learning and Neural Networks for BI
5.2 Generative AI for BI
5.3 Advanced AI Techniques
5.4 Hands-On Activity
Module 6: Statistical Analysis with AI Tools
6.1 Statistical Analysis for BI
6.2 Time Series Analysis
6.3 Hands-On Activity
Module 7: AI-Powered Business Intelligence Tools
7.1 AI in BI Platforms
7.2 Power BI Essentials
7.3 Tableau Essentials
7.4 Hands-On Activity
Module 8: Prompt Engineering for AI-Driven BI
8.1 Introduction to Prompt Engineering
8.2 Crafting Effective Prompts
8.3 Hands-On Activity
Module 9: Communication Skills
9.1 Data Storytelling & Communication
9.2 Solution Presentation
Module 10: Capstone Project
Capstone Project 1