Curriculum
Course: Power BI Essentials
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Curriculum

Power BI Essentials

Introduction to Power BI

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Importing Datasets and Modelling

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Querries in Power BI

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Plotting

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Saving and Exporting in Power BI

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Advanced Data Modeling and Relationships in Power BI

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Using Filters and Slicers in Power BI

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Introduction to DAX

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Creating Interactive Dashboards

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Publishing and Sharing Reports

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Power BI and AI Features

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Power BI Security and Governance

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Automating Reports and Power BI service Features

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FINAL POWER BI PROJECT AND CASE STUDY

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Text lesson

Power BI and AI Features

Power BI and AI Features

Introduction to Power BI

Power BI is a powerful business analytics tool developed by Microsoft that enables users to visualize data, share insights across an organization, and embed them in an app or website. It offers interactive dashboards, real-time analytics, and deep integration with various data sources.

Key Components of Power BI

  1. Power BI Desktop – A desktop application for data analysis and report creation.
  2. Power BI Service – A cloud-based service for sharing and collaboration.
  3. Power BI Mobile – A mobile application for accessing reports on the go.
  4. Power BI Report Server – An on-premises solution for hosting Power BI reports.
  5. Power BI Gateway – A tool to connect on-premises data to the cloud service.

AI Features in Power BI

Power BI incorporates Artificial Intelligence (AI) to enhance data insights and analytics capabilities. These features include:

1. AI-Driven Visualizations

  • Key Influencers: Uses AI to analyze data and identify key factors influencing outcomes.
  • Decomposition Tree: Allows users to break down a metric and understand the underlying drivers.
  • Smart Narratives: Automatically generates text summaries for reports and dashboards.

2. AI-Powered Data Processing

  • Automated Machine Learning (AutoML): Helps users build predictive models without requiring deep expertise in data science.
  • Cognitive Services Integration: Allows users to apply AI models for text analytics, sentiment analysis, and image recognition.
  • Azure Machine Learning Integration: Enables seamless deployment of complex ML models into Power BI.

3. Natural Language Processing (NLP)

  • Q&A Feature: Allows users to ask questions in natural language and receive answers in the form of visuals.
  • Power BI Copilot: Uses AI to assist in data analysis, summarization, and insights generation.

Real-World Examples

  1. Retail Analytics: A retail company uses Power BI AI features to analyze customer purchasing trends and predict future demand.
  2. Healthcare Insights: Hospitals use AI-driven visualizations to track patient data, predict disease outbreaks, and improve operational efficiency.
  3. Financial Forecasting: Banks integrate AI models to analyze financial transactions and detect fraudulent activities.
  4. Manufacturing Optimization: AI-based insights help manufacturers predict equipment failures and optimize production schedules.
  5. Marketing Campaign Analysis: AI helps businesses assess campaign effectiveness and optimize marketing strategies.

Advantages of Using AI in Power BI

  • Improved Decision Making: AI-driven insights provide deeper analytics for better strategic decisions.
  • Automation: Reduces manual efforts by automating data preparation and analysis.
  • Enhanced Data Interpretation: AI-generated narratives make complex data easier to understand.
  • Scalability: Can handle large datasets efficiently with cloud-based AI models.
  • Integration: Works seamlessly with Azure AI and other Microsoft services.

Conclusion

Power BI, combined with AI, transforms traditional data analytics into a more dynamic and predictive approach. With AI-powered visualizations, machine learning integration, and natural language queries, Power BI enables organizations to make data-driven decisions more effectively.

 

By leveraging Power BI’s AI capabilities, businesses can extract meaningful insights, improve efficiency, and stay ahead in today’s data-driven world.