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Microsoft Power BI Data Analyst (PL-300T00)

SS Course: GK821510

Course Overview

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The course will show how to access and process data from a range of data sources including both relational and non-relational sources. Finally, this course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution.

                                                                  

Scheduled Classes

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02/14/23 - GVT - Virtual Classroom - Virtual Instructor-Led
04/03/23 - GVT - Virtual Classroom - Virtual Instructor-Led
04/10/23 - GVT - Virtual Classroom - Virtual Instructor-Led
05/01/23 - GVT - Virtual Classroom - Virtual Instructor-Led
05/15/23 - GVT - Virtual Classroom - Virtual Instructor-Led
06/05/23 - GVT - Virtual Classroom - Virtual Instructor-Led
06/12/23 - GVT - Virtual Classroom - Virtual Instructor-Led
06/20/23 - GVT - Virtual Classroom - Virtual Instructor-Led
07/10/23 - GVT - Virtual Classroom - Virtual Instructor-Led
07/17/23 - GVT - Virtual Classroom - Virtual Instructor-Led

Outline

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Module 1 :Discover data analysis

  • Learn about the roles in data.
  • Learn about the tasks of a data analyst.

Module 2 :Get started building with Power BI

  • Learn how Power BI services and applications work together.
  • Explore how Power BI can make your business more efficient.
  • Learn how to create compelling visuals and reports.

Module 3 :Get data in Power BI

  • Identify and connect to a data source
  • Get data from a relational database, like Microsoft SQL Server
  • Get data from a file, like Microsoft Excel
  • Get data from applications
  • Get data from Azure Analysis Services
  • Select a storage mode
  • Fix performance issues
  • Resolve data import errors

Module 4 :Clean, transform, and load data in Power BI

  • Resolve inconsistencies, unexpected or null values, and data quality issues.
  • Apply user-friendly value replacements.
  • Profile data so you can learn more about a specific column before using it.
  • Evaluate and transform column data types.
  • Apply data shape transformations to table structures.
  • Combine queries.
  • Apply user-friendly naming conventions to columns and queries.
  • Edit M code in the Advanced Editor.

Module 5 :Design a data model in Power BI

  • Create common date tables
  • Configure many-to-many relationships
  • Resolve circular relationships
  • Design star schemas

Module 6 :Introduction to creating measures using DAX in Power BI

  • Build quick measures.
  • Create calculated columns.
  • Use DAX to build measures.
  • Discover how context affects DAX measures.
  • Use the CALCULATE function to manipulate filters.
  • Implement time intelligence by using DAX.

Module 7 :Optimize a model for performance in Power BI

  • Review the performance of measures, relationships, and visuals.
  • Use variables to improve performance and troubleshooting.
  • Improve performance by reducing cardinality levels.
  • Optimize DirectQuery models with table level storage.
  • Create and manage aggregations.

Module 8 :Work with Power BI visuals

  • Add visualization items to reports.
  • Choose an effective visualization.
  • Format and configure visualizations.
  • Import a custom visual.
  • Add an R or Python visual.

Module 9 :Create a data-driven story with Power BI reports

  • Design a report layout.
  • Add buttons, bookmarks, and selections.
  • Design report navigation.
  • Use basic interactions.
  • Use advanced interactions and drillthrough.
  • Configure conditional formatting.
  • Apply slicing, filtering, and sorting.
  • Publish and export reports.
  • Comment on reports.
  • Use Performance analyzer to tune reports.
  • Optimize reports for mobile use.

Module 10 :Create dashboards in Power BI

  • Set a mobile view.
  • Add a theme to the visuals in your dashboard.
  • Configure data classification.
  • Add real-time dataset visuals to your dashboards.
  • Pin a live report page to a dashboard.

Module 11 :Perform analytics in Power BI

  • Explore statistical summary.
  • Identify outliers with Power BI visuals.
  • Group and bin data for analysis.
  • Apply clustering techniques.
  • Conduct time series analysis.
  • Use the Analyze feature.
  • Use advanced analytics custom visuals.
  • Review Quick insights.
  • Apply AI Insights.

Module 12 :Work with AI visuals in Power BI

  • Use the Q&A visual.
  • Find important factors with the Key influencers visual.
  • Use the Decomposition Tree visual to break down a measure.

Module 13 :Create and manage workspaces in Power BI

  • Distribute a report or dashboard.
  • Monitor usage and performance.
  • Recommend a development life cycle strategy.
  • Troubleshoot data by viewing its lineage.
  • Configure data protection.

Module 14 :Manage datasets in Power BI

  • Create dynamic reports with parameters.
  • Create what-if parameters.
  • Use a Power BI gateway to connect to on-premises data sources.
  • Configure a scheduled refresh for a dataset.
  • Configure incremental refresh settings.
  • Manage and promote datasets.
  • Troubleshoot service connectivity.
  • Boost performance with query caching (Premium).

Module 15 :Implement row-level security

  • Configure row-level security by using a static method.
  • Configure row-level security by using a dynamic method.

    Prerequisites

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    Successful Data Analysts start this role with experience of working with data in the cloud.

    Specifically:

    • Understanding core data concepts.
    • Knowledge of working with relational data in the cloud.
    • Knowledge of working with non-relational data in the cloud.
    • Knowledge of data analysis and visualization concepts.

    You can gain the prerequisites and a better understanding of working with data in Azure by completing Microsoft Azure Data Fundamentals before taking this course.

      Who Should Attend

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      The audience for this course are data professionals and business intelligence professionals who want to learn how to accurately perform data analysis using Power BI. This course is also targeted toward those individuals who develop reports that visualize data from the data platform technologies that exist on both in the cloud and on-premises.