power bi advanced
Mastering Advanced Power BI Techniques
power bi advanced
Power BI Advanced refers to the more complex features and capabilities of Microsoft's Power BI, a business analytics service. It encompasses advanced data transformation techniques using Power Query, complex DAX (Data Analysis Expressions) calculations for enhanced data modeling, and the creation of sophisticated visualizations and dashboards that effectively communicate insights. Advanced users can implement Row-Level Security (RLS) for data access management, utilize the Power BI Service for collaboration and distribution of reports, and leverage AI capabilities for predictive analytics and machine learning integration. Additionally, advanced scenarios might involve integrating Power BI with other Microsoft tools (like Azure and Excel) and utilizing custom visuals to enhance the reporting experience, ensuring that organizations can derive deeper insights from their data.
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1 - Data Modeling: Teach students how to create efficient data models, including the importance of relationships, star schema, and normalization vs. denormalization.
2) DAX (Data Analysis Expressions): Introduce advanced DAX formulas for complex calculations, aggregations, and the creation of calculated columns and tables.
3) Custom Visuals: Explore the use of custom visuals from the Power BI marketplace to enhance reports and dashboards.
4) Hierarchies: Discuss how to create and use hierarchies in Power BI to enable drill down capabilities, improving user interaction with data.
5) Row Level Security (RLS): Explain the implementation of RLS to restrict data access for different users and enhance data security.
6) Power Query Advanced Techniques: Teach advanced data transformation techniques using Power Query M language for complex data cleansing and shaping.
7) Bookmarks and Selections: Show how to use bookmarks and selection pane to create interactive reports and dynamic storytelling in Power BI.
8) What If Analysis: Guide students in creating what if scenarios to analyze data under different assumptions and business cases.
9) Performance Optimization: Educate on techniques to optimize Power BI files, including reducing data load time and improving report performance.
10) APIs and Power BI Integration: Discuss how to use REST APIs to integrate Power BI with other applications and pull data dynamically.
11) Dataflows: Introduce dataflows for building reusable datasets and establishing a centralized data preparation workflow.
12) Publishing and Sharing: Walk students through the publishing process to Power BI Service, as well as best practices for sharing and collaborating on reports.
13) Real Time Data Streaming: Explore options for streaming data into Power BI, including real time dashboards and integrating with Azure.
14) Using R and Python: Cover how to leverage R and Python scripts within Power BI for advanced analytics and custom visualizations.
15) Paginated Reports: Explain the creation of paginated reports for pixel perfect, print friendly output suitable for operational reporting.
16) Power BI Embedded: Discuss how to embed Power BI reports into applications and websites for extended data sharing.
17) Power BI Mobile: Teach best practices for designing reports optimized for mobile viewing and the Power BI mobile app usage.
These points collectively provide an in depth exploration of Power BI Advanced topics and can help students gain a robust skillset for data analysis and visualization using the platform.
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