Recommended 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.
Course Outline:
Lesson 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
Lesson 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
Lesson 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
Lesson 4: Machine Learning (ML) for Business Intelligence
- 4.1 ML Models for BI
- 4.2 Hands-On Activity
Lesson 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
Lesson 6: Statistical Analysis with AI Tools
- 6.1 Statistical Analysis for BI
- 6.2 Time Series Analysis
- 6.3 Hands-On Activity
Lesson 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
Lesson 8: Prompt Engineering for AI-Driven BI
- 8.1 Introduction to Prompt Engineering
- 8.2 Crafting Effective Prompts
- 8.3 Hands-On Activity
Lesson 9: Communication Skills
- 9.1 Data Storytelling & Communication
- 9.2 Solution Presentation
Lesson 10: Capstone Project
- Capstone Project 1
- Capstone Project 2
- Capstone Project 3
