Power BI

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This comprehensive course introduces you to the world of Power BI, a powerful business analytics tool by Microsoft. From the basics of data connection to advanced data modeling and visualization techniques, you'll gain the skills to transform raw data into actionable insights. Through hands-on exercises and guided projects, you'll learn how to navigate the Power BI interface, create effective data models, and harness the potential of Data Analysis Expressions (DAX).

Course Goals:

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

  • Navigate the Power BI interface, utilizing its ribbons and features effectively.
  • Establish connections to various data sources using different data connectors.
  • Apply fundamental transformations to shape and prepare data for analysis.
  • Build robust data models using relationships, fact and dimension tables, and hierarchies.
  • Utilize DAX expressions to create calculated columns and measures.
  • Develop a strong understanding of data normalization and database principles.
  • Execute data loading, cleaning, and transformation processes using Power BI.
  • Merge, append, and pivot data for optimal analysis.
  • Create insightful data visualizations and dashboards that convey valuable insights.

Course Objectives:

Throughout the course, you will achieve the following objectives:

Module 1: Creating Relationships & Data Models in Power BI

  • Explore the Power BI interface and its various ribbons.
  • Review project source data and identify key data connectors.
  • Utilize the Query Editor for data transformation tasks.
  • Configure Power BI data source settings for optimized analysis.
  • Define hierarchies to establish meaningful data relationships.
  • Gain an introduction to data modeling and the principles of database normalization.
  • Manage and edit tables efficiently within Power BI.
  • Create and manage relationships among data tables.
  • Understand the flow of filters and their impact on visualization.

Module 2: Connecting & Shaping Data

  • Learn about the different types of data connectors available in Power BI.
  • Utilize the Power Query Editor for basic table transformations.
  • Extract data from a variety of sources including files, CSV, and the web.
  • Implement data refresh mechanisms for keeping information up to date.

Module 3: Creating a Data Model

  • Explore the concepts of database normalization.
  • Understand the distinction between fact and dimension tables.
  • Learn about primary and foreign keys in the context of Power BI.
  • Choose between creating table relationships and merging tables.
  • Effectively manage and edit relationships within Power BI.
  • Categorize and format data for consistent analysis.
  • Create hierarchies to enhance data exploration and navigation.

Module 4: Calculated Fields with DAX

  • Dive into Data Analysis Expressions (DAX) and its significance.
  • Compare DAX with other query languages.
  • Gain an introduction to calculated columns and measures.
  • Differentiate between implicit and explicit measures.
  • Utilize quick measures for rapid analysis.
  • Learn DAX syntax, operators, and functions.
  • Understand the role of the RELATED function in joining data.
  • Master the CALCULATE function for tailored calculations.

Module 5: Prepare, Clean, Transform, and Load Data using Power BI (Guided Project)

  • Practice splitting and renaming columns for data clarity.
  • Add new columns for enhanced analysis.
  • Effectively remove columns and adjust data types.
  • Merge and append datasets to consolidate information.
  • Unpivot and pivot data to reshape for analysis.

Throughout the course, you will engage in hands-on activities and a guided project to solidify your understanding of Power BI concepts and techniques. By the end, you'll be equipped to wield Power BI effectively and contribute valuable insights to your organization or projects.

 

Module 1: Creating Relationships & Data Models in Power BI

  • Explore the Power BI interface and its various ribbons.
  • Review project source data and identify key data connectors.
  • Utilize the Query Editor for data transformation tasks.
  • Configure Power BI data source settings for optimized analysis.
  • Define hierarchies to establish meaningful data relationships.
  • Gain an introduction to data modeling and the principles of database normalization.
  • Manage and edit tables efficiently within Power BI.
  • Create and manage relationships among data tables.
  • Understand the flow of filters and their impact on visualization.

Module 2: Connecting & Shaping Data

  • Learn about the different types of data connectors available in Power BI.
  • Utilize the Power Query Editor for basic table transformations.
  • Extract data from a variety of sources including files, CSV, and the web.
  • Implement data refresh mechanisms for keeping information up to date.

Module 3: Creating a Data Model

  • Explore the concepts of database normalization.
  • Understand the distinction between fact and dimension tables.
  • Learn about primary and foreign keys in the context of Power BI.
  • Choose between creating table relationships and merging tables.
  • Effectively manage and edit relationships within Power BI.
  • Categorize and format data for consistent analysis.
  • Create hierarchies to enhance data exploration and navigation.

Module 4: Calculated Fields with DAX

  • Dive into Data Analysis Expressions (DAX) and its significance.
  • Compare DAX with other query languages.
  • Gain an introduction to calculated columns and measures.
  • Differentiate between implicit and explicit measures.
  • Utilize quick measures for rapid analysis.
  • Learn DAX syntax, operators, and functions.
  • Understand the role of the RELATED function in joining data.
  • Master the CALCULATE function for tailored calculations.

Module 5: Prepare, Clean, Transform, and Load Data using Power BI (Guided Project)

  • Practice splitting and renaming columns for data clarity.
  • Add new columns for enhanced analysis.
  • Effectively remove columns and adjust data types.
  • Merge and append datasets to consolidate information.
  • Unpivot and pivot data to reshape for analysis.
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