Instructor-led 4-days

Course Description:

This four-Part intensive course provides a comprehensive understanding of how to integrate artificial intelligence (AI) tools and methodologies with business analytics using Microsoft Power BI. Participants will explore the latest AI-driven features in Power BI and learn how to build intelligent analytics solutions. With hands-on projects, case studies, and interactive sessions, the course covers data preparation, AI model integration, data visualization, predictive analytics, and decision-making support. By the end of this course, participants will be proficient in using AI to drive actionable insights and enhance business decision-making through Power BI.

Course Objectives:

By the end of the course, participants will be able to:

1. Understand the fundamentals of AI in business analytics and how it integrates with Power BI.
2. Leverage AI-driven capabilities within Power BI for data preparation, visualization, and analysis.
3. Apply AI models in Power BI for predictive analytics and data-driven decision support.
4. Develop end-to-end business analytics solutions that incorporate AI insights for real-world applications.
5. Enhance reporting and analytics with automated insights, forecasting, and natural language processing (NLP).

Recommended Prerequisites:

• No prior programming knowledge is required, though familiarity with data science concepts can be helpful

Target Audience:

• Business analysts, data analysts, business intelligence professionals, and managers interested in using AI-driven insights within Power BI

Part 1: Introduction to AI in Business Analytics and Power BI

• Module 1: Overview of AI in Business Analytics
  • Importance of AI in modern business analytics
  • Key AI concepts: Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision
  • Introduction to Power BI and its AI capabilities
• Module 2: Power BI Essentials for AI-Driven Analytics
  • Setting up Power BI: Workspace, datasets, and dashboard essentials
  • Data connectivity, transformation, and loading (ETL) basics
  • Using Power Query for data transformation
• Module 3: Preparing Data for AI-Driven Analytics
  • Data quality assessment and cleaning techniques
  • Data preprocessing: handling missing values, outliers, and feature engineering
  • Creating data models in Power BI
• Module 4: AI Features in Power BI
  • Introduction to AI visuals: Decomposition tree, Key influencers, and Q&A
  • Hands-on: Building a sample AI-powered report in Power BI
  • Case Study: How AI-driven insights enhance a company’s sales analysis

Part 2: Integrating AI Models with Power BI

• Module 1: Fundamentals of AI Models in Power BI
  • Overview of AI models: Supervised and unsupervised learning
  • Introduction to Azure Machine Learning and Power BI integration
• Module 2: Using Cognitive Services in Power BI
  • Integrating text analytics, sentiment analysis, and language detection
  • Hands-on: Applying sentiment analysis on customer feedback data in Power BI
  • Discussion: Benefits and limitations of cognitive services for business applications
• Module 3: Custom AI Models in Power BI
  • Importing pre-trained models into Power BI
  • Setting up R and Python scripts in Power BI for custom analytics
  • Hands-on: Building and deploying a custom ML model for a predictive sales forecast
• Module 4: Real-World Use Cases
  • Case studies of AI and ML in customer segmentation, marketing optimization, and inventory management
  • Group Exercise: Designing an AI-based solution for a provided business scenario

Part 3: Advanced AI Features and Data Visualization in Power BI

• Module 1: Advanced AI-Driven Visualizations in Power BI
  • Deep dive into Key Influencers and Decomposition tree visuals
  • Using AI insights to reveal data patterns and trends
• Module 2: Predictive Analytics with Power BI
  • Implementing forecasting models within Power BI
  • Applying time series analysis to business scenarios
  • Hands-on: Building a predictive model to forecast monthly revenue
• Module 3: Using Natural Language Processing (NLP) in Power BI
  • Leveraging NLP for data exploration: Q&A, text-based insights
  • Hands-on: Integrating NLP with Power BI for automated reporting
  • Case Study: Using NLP to analyze and visualize customer feedback trends
• Module 4: Best Practices in AI-Driven Visual Storytelling
  • Effective dashboard design with AI insights
  • Storytelling techniques to enhance comprehension of AI-generated insights
  • Group Project: Design a compelling AI-driven story for business decision-makers

Part 4: Building and Presenting an AI-Powered Business Analytics Solution

• Module 1: Building a Comprehensive Solution in Power BI
  • Recap of all tools and techniques covered in the course
  • Step-by-step guide to building an AI-enhanced end-to-end solution in Power BI
• Module 2: Project Work – Designing a Real-World AI Solution
  • Defining project scope and objectives
  • Working with real or simulated datasets to implement AI-driven analytics
  • Integrating data, AI models, and interactive visuals
• Module 3: Final Presentation and Feedback
  • Each participant/team presents their project to the group
  • Feedback and discussion on each solution’s design, functionality, and business impact
• Module 4: Course Wrap-Up and Future of AI in Business Analytics
  • Key takeaways and resources for continuous learning
  • Discussion on upcoming AI trends in business intelligence and analytics
  • Q&A and course evaluation