This recording details a Power BI training session, progressing through four phases: data discovery and acquisition, data cleaning using Power Query, data modeling and the creation of measures using DAX, and finally, data visualization and report publishing to the Power BI service. The session uses an educational dataset and features multiple instructors guiding participants through each stage. Specific techniques covered include data cleansing, creating and modifying measures (both implicit and explicit), using hierarchies to improve data organization, applying themes for consistent report design, employing custom visuals and the Q&A feature, and leveraging co-pilot for report generation suggestions. The final segment focuses on sharing and collaboration within the Power BI service, including scheduling data refreshes.

Power BI Beginner to Pro Study Guide

Quiz

Instructions: Answer the following questions in 2-3 sentences each.

1. Why is the Power BI Beginner to Pro session re-recorded regularly?
2. What is the primary reason organizations choose to use a tool like Power BI?
3. What specific data is used in this year’s Power BI Beginner to Pro session?
4. What is the Power Query Editor, and why is it important in Power BI?
5. What is the difference between “null” and “blank” values in data, and why is it important in Power BI?
6. What does DAX stand for and what is its function within Power BI?
7. What is the report view in Power BI and what is it used for?
8. What is a one-to-many relationship in Power BI data modeling, and how is it represented?
9. What are implicit and explicit measures in Power BI, and what is the difference between them?
10. What are some of the benefits of using hierarchies in Power BI data modeling?

Quiz Answer Key

1. The Power BI Beginner to Pro session is re-recorded regularly due to the frequent updates and changes that occur within the Power BI platform. These changes necessitate the re-recording of the sessions to provide the most up-to-date and accurate instruction to users.
2. Organizations choose tools like Power BI primarily to make better data-driven decisions, ensuring that significant company choices are based on collected and analyzed data, allowing for better strategic planning.
3. This year’s Power BI Beginner to Pro session uses education data focused on math and reading test scores, and the program simulates a national tutoring program to determine effectiveness. This data is not real and is for demo purposes.
4. The Power Query Editor is a separate window within Power BI that is used for data cleaning, data preparation, and data transformations. It allows users to shape and modify data before loading it into the data model.
5. A “null” value represents the absence of a value, while a “blank” value is an empty space, not the absence of data. Converting blanks to nulls is sometimes necessary for using Power BI transformations to properly replicate or filter data.
6. DAX stands for Data Analysis Expressions and is the formula or coding language built into Power BI. It is used to create custom calculations, analyze data, and derive new insights from your data.
7. The report view in Power BI is the primary interface where users create data visualizations. It contains the visualization pane with various chart options, and is where you build out a report made of one or more pages.
8. A one-to-many relationship is a type of connection between two tables where one record in the first table can be related to multiple records in the second table. It is represented by a “1” on one table and an asterisk or star on the “many” side.
9. Implicit measures are automatic aggregations that Power BI applies to numeric columns, like sum or average. Explicit measures are custom calculations created by the user with DAX, that provide more flexibility.
10. Hierarchies in Power BI data modeling allow users to drill down from higher levels to lower levels of granularity for a more in-depth analysis. They enhance user experience and improve data exploration.

Essay Questions

Instructions: Write a short essay on one of the following topics.

1. Discuss the different phases of the Power BI lifecycle, detailing the processes involved in each phase and their importance in the development of effective Power BI solutions.
2. Explain the role of data preparation and cleaning within Power BI, including the use of the Power Query Editor, and analyze why it’s important for effective data analysis and visualization.
3. Analyze the use of DAX in Power BI and how it allows users to create new calculated columns and measures. Describe how this improves analytical capabilities beyond standard visualizations.
4. Explore the importance of data visualization in Power BI, providing several examples of good and poor visualization choices and their impact on the usability and understandability of a Power BI report.
5. Explain the differences between reports and dashboards within Power BI, and discuss the advantages of publishing data to the Power BI service.

Glossary

• Data-Driven Decisions: Decisions based on data analysis, not personal feelings or guesses.
• Power BI Desktop: A free, Windows-based application for building Power BI reports.
• Power BI Service: The cloud-based version of Power BI where you publish reports and data.
• Data Model: The structure of data in Power BI, including tables, relationships, and measures.
• Power Query Editor: A data transformation tool within Power BI used to clean and shape data.
• Null Value: The absence of a value in a data record.
• Blank Value: An empty cell in a data record.
• DAX (Data Analysis Expressions): The formula language used for creating calculated columns and measures.
• Report View: The interface in Power BI Desktop for building data visualizations.
• One-to-Many Relationship: A connection between tables where one record in a table may relate to many records in another.
• Implicit Measure: A built in, automatic summarization or aggregation of data, by Power BI, based on the type of column used.
• Explicit Measure: A custom calculated field created with DAX to derive new insights.
• Visualizations: Graphical representations of data, such as charts and graphs.
• Hierarchy: A way to organize related data that allows users to drill down from higher levels of summarization to greater levels of detail.
• Theme: A predefined set of colors, fonts, and styles that you can apply to a Power BI report.
• Conditional Formatting: Formatting data cells based on specific conditions, often using color scales to highlight significant data.
• Custom Visual: Visualizations that have been created by Microsoft or community members that can be added to Power BI.
• Tool Tip: A small box of information that pops up when you hover over a data point in a visual.
• Semantic Model: A cloud-based database that represents a specific area of knowledge in a way that users can query it directly using natural language.
• Workspace: A container in the Power BI service that holds reports, data, and other content.
• My Workspace: Personal workspace for individuals to publish reports and data for personal use only.
• Fabric: A Microsoft platform encompassing services like Power BI and Azure.
• Co-Pilot: An AI assistant that helps users build reports, write DAX, or refine data.

Power BI Training: Beginner to Pro

Okay, here’s a detailed briefing document summarizing the key themes and ideas from the provided source, which is a transcript of a Power BI training session.

Briefing Document: Power BI Beginner to Pro Training

1. Introduction and Course Overview

• Dynamic Nature of Power BI: The training emphasizes that Power BI is constantly evolving, requiring regular updates to training materials.
• “powerbi changes on a very regular basis so because of those changes we have to re-record this session on a regular basis as well”
• New Data Set and Multiple Trainers: This year’s session features a new educational data set and multiple trainers, offering diverse perspectives on the Power BI lifecycle.
• “we’re bringing a new data set to the table through this session… we’re also going to have different teachers different trainers joining us”
• Purpose of Power BI: The primary reason to use Power BI is to enable better data-driven decisions within an organization.
• “the big reason why you’re going to choose a tool like powerbi is to help you and to help your organization make better datadriven decisions”
• The Power BI Lifecycle: The course will walk users through the complete data cycle: collecting, organizing, presenting, and ultimately making decisions based on data.

2. Core Components of Power BI

• Power BI Desktop (Free): The primary tool for building reports, downloadable for free on Windows devices (with some caveats for Mac users).
• “this is a free download which is amazing… you have to have a device that’s running Windows”
• Data Collection: Data will be pulled from GitHub, a common repository platform.
• “we have these conveniently stored over on GitHub… we are able to point to data on the web”
• Power Query Editor: A separate window where data cleaning, preparation, and transformation occur.
• “select transform data… that’s going to launch the power query editor for us”
• “power query editor will only bring in a sample of your data about a thousand rows”
• Data Modeling: Involves building relationships between multiple datasets using DAX calculations.
• “in data modeling right that is where you are going to bring your nice cleaned up data from powerquery and then if you have multiple data sets you need to build a relationship”
• “Dax is the language essentially your formula your coding language that is built into powerbi”
• Report View (Data Visualization): This is where users create visuals (charts, tables, etc.) to represent the data.
• “this is called your report View and here in your report view that is where you’re going to see your visualization pain”
• Publishing and Sharing: The final phase where reports are made accessible to others.

3. Data Cleansing and Preparation

• Data Imperfections: The training emphasizes that real-world data is often messy and needs cleaning.
• “data rarely comes to us in the format we need in for reporting… there are errors and consistencies”
• Null vs. Blank Values: A distinction is made between “null” (absence of a value) and “blank” (an empty value). Null values are needed for certain transformations.
• “nulls when you see null in your data that is the the absence of a value… when you see blank it’s like an imaginary space”
• Replacing Blank Values with Null: Blank values are replaced with null to enable fill-down operations which allows for the replication of values in ragged reports.
• “take those blank values and replace them with null… having it as a null value will help us”
• Fill Down Transformation: A technique to replicate values down columns (used for District, City, State).
• “we need to use that fill down option”
• Creating Combined Columns: Using “Column from Examples” to combine city and state into a single, unique column (for map visualizations).
• “column from examples… allows you to create a new column based on a sample value that you provide”

4. Data Modeling and Relationships

• Table Relationships: Building connections between tables (e.g., student data and test scores) to enable meaningful analysis.
• “if I have a relationship between my tables there should be a line connecting”
• One-to-Many Relationships: The concept of a relationship where one record in one table can relate to multiple records in another table (e.g. one student may have multiple test scores).
• “A one to mini relationship just depends on the amount of the values that are in each of the columns”
• Hiding Key Columns: Hiding ID or other identifying columns in the model as they may be confusing for report viewers.
• “I typically like to hide my key columns because you’re not actually going to use them very often in your visuals”
• Summarization Issues: By default, Power BI will automatically sum numeric values. It’s necessary to switch many of these to average, none, or other aggregations.
• “by default powerbi is going to apply summarization… we can change that summarization”

5. DAX Calculations and Measures

• Implicit vs. Explicit Measures: Implicit measures (automatic calculations by Power BI) are contrasted with explicit measures (calculations written by the user).
• “powerbi has created an implicit measure… we can also make explicit measures”
• Basic DAX Formulas: How to write formulas using functions like AVERAGE, referencing tables and columns.
• “I’m finding the average of my MTH score in my test T my test scores table”
• Quick Measures: A feature that generates more complex DAX code for common calculations.
• “quick measures are just an easier way to write decks… if you know what you’re looking for you can try quick measures”
• Modifying Quick Measures: The ability to adjust and customize DAX code generated by quick measures to better fit reporting needs.
• “you can alter these it’s not like a oneandone like you put it in there and that’s all there is to it”

6. Hierarchies and Data Organization

• Hierarchies: Creating drill-down paths for data exploration (e.g., state to district) to aid user interaction.
• “you need to think about that very end user the last person who’s going to consume your data do you want them to have that capability to dive deep”
• Folders: Organizing fields in the data pane for easier access and a cleaner workspace.
• “I like to create folders for my specific different columns… it just makes it easier to create what I want to”

7. Data Visualization Best Practices

• Data Storytelling: Framing reports to convey a specific message through a series of impactful visuals.
• “data storytelling comes into play here… framing your report to convey a certain message”
• Focusing on Key Questions: The need to focus on 3-4 key questions a report should answer.
• Appropriate Visual Selection: Discussing the types of visuals to be used for different purposes.
• Visual Formatting: Utilizing themes, borders, radius, colors to make visuals more impactful.
• “a border will literally draw a line around your report visuals… radius is actually going to take your visualization… and round the corners”
• Custom Backgrounds: Using a background image to guide report design and visualization placement.
• “bring in your own custom backgrounds… it’s already designed to help us visualize where things go”
• Data Labels and Details: Adding labels, details, and tooltips to make visuals more informative.
• “this is actually going to Auto but if I had Millions I could change it to millions billions trillions of dollars thousands… I can see that it’s letting me know the exact value”
• Conditional Formatting: Applying formatting based on data values to highlight important information (gradients).
• “conditionally format this background color easiest way to go about this is a gradient”

8. Power BI Service and Sharing

• Power BI Service: The web version of Power BI, for publishing and sharing reports.
• “powerbi service is really just the web version of powerbi… it’s safe secure”
• Workspaces: Organizing reports and data within the service.
• “a workspace this is where you are able to publish all of your reports”
• License Levels: The differences between Power BI Pro and Premium licensing.
• “there are two main license levels to have which are powerbi pro… and then there is powerbi premium”
• Sharing Reports: Various options for sharing a report to different groups or individuals.
• “with sharing I have the ability to give access to this report to really anyone I want”
• Dashboards vs Reports: The distinction between dashboards and reports, with dashboards being a curated view of multiple reports.

9. Advanced Features and Tools

• Custom Visuals: Exploring third party visuals for advanced visualization needs.
• “we have a wide range of custom visuals that can be found under this option right here it’s called get more visuals”
• Q&A Functionality: Using natural language queries to generate visuals and explore data.
• “with Q&A I also have the ability to tell it what visual I want”
• Tooltips: Custom tooltips to show more detailed information on hover.
• “a tool tip is a reflection of that X and Y AIS… you can customize it”
• Co-Pilot Integration: Using AI for report design.
• “using AI to give you the ability to create a report using co-pilot functionality”
• Content Suggestion Using AI to provide recommendations for future development.
• “we’re going to get some ideas of maybe what direction we could potentially go”
• Accessibility: Prioritizing accessibility during report development.
• “make a much better report if you design with everybody in mind regardless of their accessibility needs”

10. Key Questions to be Answered in the Course:

• Which school districts are doing best?
• What state has the best reading scores?
• Is tutoring having a positive impact?
• What is the impact of extracurriculars?

Overall Theme

This training session is a practical guide to using Power BI for data analysis and visualization. It emphasizes a structured approach, from data cleaning to creating actionable insights. It balances fundamental concepts with more advanced techniques, like DAX and custom visuals. The introduction of AI tools for report generation suggests a future direction for Power BI and data analysis.

Let me know if you have any other questions or would like any part expanded!

Power BI: Beginner to Pro Guide

FAQ: Power BI Beginner to Pro

• Why should I choose Power BI as a data analysis tool? Power BI helps you and your organization make better data-driven decisions. It allows you to collect, organize, and present data to inform significant decisions within the company. This is crucial for solving business problems effectively and making strategic choices backed by evidence.
• What is the Power BI lifecycle, and what are the different phases involved? The Power BI lifecycle consists of four main phases. First, Data Acquisition, where you connect to various data sources. Second, Data Modeling, where you clean and prepare your data using Power Query, build relationships between tables, and perform calculations with DAX. Third, Data Visualization, where you build reports using visuals. And finally, the Publishing phase, where you share reports for others to use and make decisions.
• What is Power BI Desktop and how do I get it? Power BI Desktop is a free Windows application that allows you to build Power BI reports. You can download it directly from the Microsoft Store or the provided link. If you are using a Mac you would need to use another program that allows you to run Windows. The Microsoft Store version is recommended since it stays up to date with monthly updates, but there are also other ways your organization may provide it for you.
• What is Power Query, and why is it important? Power Query is a data transformation tool within Power BI. It allows you to clean, shape, and prepare your data by correcting errors, inconsistencies and applying transformations. It is important because data rarely comes in the exact format needed for reporting, so you will always need to modify it and get it report-ready within Power Query.
• What is DAX, and how is it used? DAX, or Data Analysis Expressions, is the formula language within Power BI used to perform calculations and create custom measures. DAX is very similar to Excel formulas. You can use DAX for everything from complex statistical analysis, to creating a new column, to defining how something sums. It is a powerful language for enhancing your data models.
• What’s the difference between a null value and a blank value? A null value represents the absence of a value in a field. A blank value is like an empty space. When Power Query encounters blanks it will not allow you to use certain transforms to help fill in missing values. You typically need to convert blanks to nulls to use specific Power Query transforms such as fill down.
• What are hierarchies in Power BI, and how are they useful? Hierarchies in Power BI allow you to group data into levels for drilling down. For example, a calendar hierarchy would consist of Year > Quarter > Month > Day. Similarly, you could have a geography hierarchy such as State > District. This grouping enables end users to navigate data from broad categories to more detailed ones very easily and intuitively.
• How do you share Power BI reports with others, and what are workspaces? To share a Power BI report you need to publish your work into a workspace in powerbi.com. Workspaces are like folders in the cloud for publishing reports. There are a couple of licenses available (Power BI Pro and Power BI Premium), each offering different capabilities. Inside workspaces you are able to share your work, manage access, and organize reports. Every user has an individual workspace called “My Workspace” where they can develop reports that are for their own use.

Learn with the Nerds: Power BI Beginner to Pro

The provided sources discuss a Power BI training session, “Learn with the Nerds Power BI Beginner to Pro,” which covers various aspects of using Power BI, from planning a project to sharing the results [1, 2]. Here’s a breakdown of the training:

• Course Overview: The training is designed to guide users from a beginner level to a more proficient level in Power BI. It uses a new educational data set and features multiple trainers [1]. The course aims to help individuals and organizations make better data-driven decisions [1].
• Training Agenda: The session is divided into several key areas [2]:
• Planning and Design: This initial phase focuses on the importance of planning a Power BI project before beginning any work in the tool. It emphasizes defining the end goal and understanding what data is needed to achieve that goal [2, 3].
• Data Cleansing and Shaping: This section covers how to connect to data sources and ensure the data is accurate, using the Power Query Editor [2, 4].
• Data Modeling: The training covers how to organize data, create relationships between different data sources, and use DAX (Data Analysis Expressions) [2].
• Data Visualization: This is the part of the training that focuses on how to build engaging reports, use storytelling capabilities, and use the co-pilot feature [2].
• Data Sharing: The final section shows how to share results, schedule data refreshes, and set security measures [2].
• Key Concepts and Tools Covered:
• Power BI Desktop: The training focuses on using the Power BI Desktop application, which is a free download for Windows users [5, 6].
• Power Query Editor: This tool is used for data cleansing and shaping [4]. It is a separate window that opens within Power BI [7].
• Data Modeling: Involves creating relationships between tables and building a strong data model for data accuracy and consistency [2, 8, 9].
• DAX: A formula language used for calculations in Power BI [2, 10, 11].
• Power BI Service: The web version of Power BI (powerbi.com), where reports are published for sharing and collaboration [12, 13].
• Workspaces: Used to organize reports and data in the Power BI service. Each user has a personal workspace called “My workspace” [14, 15]
• Hands-On Activities: The training involves practical steps such as connecting to data sources, using the Power Query editor, building visualizations, and sharing reports [7, 16-20].
• Importance of Data Cleansing: The training emphasizes the importance of cleaning and preparing data before creating visualizations to avoid errors in reporting [4, 7].
• Importance of Planning: The training emphasizes that beginning a project with a plan, including knowing the end goal and what you are trying to achieve, is essential to avoid wasting time [3].
• Data Storytelling: The training also emphasizes framing the report to convey a message and focusing on key questions that the data can answer [21].
• On-Demand Learning: The training session is part of a larger on-demand learning platform, offering various courses on Power BI including advanced data shaping, data modeling, and DAX [8, 22-27]. The source mentions that the on-demand platform has free courses, as well as paid classes that can be accessed for free for a limited time [28].
• Preview Features: The training uses the “on-object interaction” preview feature in Power BI, to enhance the interface of the tool [16, 29, 30].
• Custom Visuals: The training shows how to add custom visuals from the marketplace for a unique look and functionality [31].
• Q&A Feature: The Q&A feature is highlighted as a way to interact with data by asking questions in plain language [32, 33].
• Tooltips: The use of custom tooltips in Power BI is shown as a way to display additional information when hovering over a visual [34].
• Co-pilot: The training shows how co-pilot can build a whole page for a report based on a request, but this feature requires a special license [26, 35].
• Licensing: The training mentions different licensing levels such as Power BI Pro and Power BI Premium, with premium recently being renamed Fabric [14, 15].
• Accessibility: The training highlights the importance of accessibility and offers information about color blind themes [25].

Power BI Data Visualization Training

The sources provide extensive information on data visualization within the context of Power BI training [1, 2]. Here’s a detailed discussion of data visualization based on the sources:

• Importance of Data Visualization: The training emphasizes that while Power BI is a data visualization tool, there’s much more to it than just building visuals [1]. Effective data visualization is crucial for engagement and for making informed, data-driven decisions [1, 3]. Poorly visualized data can lead to low report usage and a failure to convey important insights [3].
• Data Storytelling: The training promotes the concept of data storytelling, which involves framing a report to convey a specific message and focusing on key questions the data can answer [4]. Instead of just randomly creating visuals, it is important to plan a report to answer specific questions [4, 5]. The training suggests focusing on 3 to 4 key questions per report to avoid overwhelming the audience [4, 5].
• Best Practices for Visual Creation:
• Plan Beforehand: It is essential to have a plan before creating visuals, including wireframing the layout [4-6]. This involves considering the placement of different types of visuals [6].
• Start with Key Questions: When building visualizations, focus on the key questions that need to be answered by the data [4, 5]. For example, in the context of the educational dataset used in the training, questions might include: What school district is performing best? What state has the best scores? Does tutoring have a positive impact? How do extracurriculars affect test scores [4]?
• Limit Visuals per Screen: It’s recommended to have around 3 to 4 main visuals per report page, along with slicers and other smaller elements [5]. Too many visuals can overwhelm users and make it difficult to extract meaningful insights [5, 7].
• Use Data Labels: Adding data labels to visuals is an easy way to add more information to your report [8]. Details can be added to labels to show other data like tutoring hours [9].
• Formatting: Power BI provides a variety of formatting options that include the ability to make changes to the background, font, and colors [8, 10, 11]. Using a border with a radius of 25 pixels is suggested to give a smooth look to visuals [11].
• Themes: Themes allow you to quickly apply a set of formatting options to an entire report, which provides consistency throughout a report [10, 11].
• Types of Visuals:
• Standard Visuals: Power BI provides a variety of built in visuals such as bar charts, line charts, pie charts, tables, and matrices [12-14].
• Custom Visuals: In addition to the standard visuals, users can add custom visuals from the marketplace, including a text filter that allows a user to search for data by text [6, 15].
• Q&A Visual: The Q&A feature is an AI-driven tool that can create visuals based on questions asked in plain language [16]. Users can type questions to get data displayed as a visual [16, 17]. This feature can be used to quickly build visuals or to get ideas when you are unsure what you want in your report [16, 17].
• Tooltips: Custom tooltips can be created to provide additional information when hovering over a visual [18]. Tooltips can be used to filter visuals on a page [18, 19].
• Data Modeling and Visuals: The training emphasizes the need to have a strong data model before beginning to build visualizations [20, 21]. Testing the data model by creating visuals and making adjustments where necessary is emphasized [22]. Measures and calculations performed in data modeling are crucial for accurate visualizations [23-25].
• Summarization: By default Power BI will summarize data, but sometimes, especially with scores, an average is more appropriate than a sum [23].
• Hierarchies: Hierarchies allow the user to drill down into the data and see more granular information such as states and districts [26, 27]. Hierarchies can also be used to organize fields [14, 28].
• Measures: The training highlights the use of both implicit and explicit measures. Power BI will automatically create implicit measures, but you can also create explicit measures by using DAX to perform calculations on your data [23, 24].
• Co-pilot: Power BI’s co-pilot feature can be used to create visuals. Co-pilot can also suggest content and create a whole report page based on a data model and a user prompt [19, 29, 30]. Co-pilot, while a useful tool for creating visualizations, should always be reviewed by the user to ensure that it is accurate and meets user requirements [30].
• Interaction Between Visuals: By default, visuals interact with each other in a Power BI report, so when you select a data point in one visual, other visuals will be filtered to reflect that selection [17].

In summary, the training highlights the importance of thinking strategically about data visualization and how it can be used to effectively communicate the meaning behind data. Effective data visualization involves planning, asking key questions, leveraging available tools, and understanding how a strong data model leads to accurate and useful reports.

Power BI Data Modeling

The sources provide detailed information on data modeling within the context of Power BI training. Here’s a comprehensive discussion of data modeling based on the sources and our conversation history:

• Importance of Data Modeling: The training emphasizes that building a strong data model is crucial for an effective Power BI report [1]. A well-structured data model ensures performance optimization, data accuracy, consistency, scalability, easier maintenance, and enhanced usability [1]. The training also highlights the fact that data modeling is not a one-time activity, but an ongoing process as you continue to build a report [1].
• Data Schemas: The training mentions two main types of data schemas:
• Star schema: This basic blueprint consists of a central fact table with dimension tables branching off it [1].
• Snowflake schema: This is an extension of the star schema where dimension tables branch off of other dimension tables, creating a snowflake effect [1].
• Key Components of Data Modeling:
• Tables: Data models usually consist of multiple tables [2]. The training uses two tables, one with test scores and one with student information, as an example [2, 3].
• Relationships: Building relationships between tables is essential to connect data and allow for proper filtering and analysis [4, 5]. Relationships are created based on columns that are common to both tables, like student ID in one table and student number in another [5]. The columns used to create a relationship must have the same data type [5].
• Cardinality: The relationship type, such as one-to-many, depends on the values in the columns [6]. In a one-to-many relationship, one table has unique values, while the related table can have multiple instances of the same value [6].
• Cross-filter direction: This describes the direction that filters will flow from one table to another [6].
• Data Types: The training highlights the importance of setting the correct data types for columns, such as text, whole number, decimal number, date, etc [7, 8]. Data types can be modified in the Power Query editor [8].
• Data Preparation:
• Power Query Editor: This tool is used for cleaning and transforming data [9, 10]. It allows users to modify data types, rename columns, remove errors, and fill in blanks [8, 9]. The Power Query editor is a separate window that opens within Power BI [10, 11].
• Column from examples: This Power Query feature can be used to create a new column based on a sample value you provide [12, 13].
• Data Profiling: The Power Query editor has options to view column quality, distribution, and profiles [7, 11]. The data profiling should be set to “entire dataset” when you are doing your data prep, but set back to “top 1,000 rows” when you are done [7].
• Data Organization:
• Hierarchies: Hierarchies allow you to group fields of information for drill-down interaction in visualizations [14, 15]. A hierarchy can be created to allow for the user to see information at a high level and then drill down to more granular detail [14]. For example, a geographical hierarchy can be created that goes from state to district [15].
• Folders: Folders are used to organize fields within a data pane, grouping similar fields such as math scores and reading scores, into separate folders [16]. Sub-folders can be created by using a backslash in the folder name [16].
• Measures and DAX:
• Implicit Measures: Power BI automatically creates implicit measures, usually sums or averages [17].
• Explicit Measures: Explicit measures are created using DAX (Data Analysis Expressions), which is the formula language for Power BI [17, 18]. DAX can be used to create custom calculations that are more efficient and quicker than implicit measures [17]. DAX can be written in the formula bar [17].
• Quick Measures: Power BI has quick measures that allow you to do simple calculations by selecting a measure and a category [19].
• Modifying DAX: The DAX from a quick measure can be modified to better suit the user’s needs [19].
• Testing the Data Model: The training emphasizes that it is important to test the data model by creating visuals and checking the results, making changes as needed [4]. Building visuals as you create the data model allows you to see any issues in the way your tables are connected and formatted [19].
• Data Modeling and Visualizations: Changes made in the data model will affect how visuals appear [6, 20]. For example, changing a summarization of reading scores to the average reading score will affect the display of the data [20]. It is also important to make sure you have relationships built between your tables so that the correct data is being displayed in visuals [4].
• Best Practices:
• Use proper capitalization, spelling, punctuation, and spacing when building your data model [21].
• Be consistent when pluralizing table names [21].
• It is best practice to rename a column in the Power Query editor rather than after you have loaded the data [22].
• When adding or changing something on the data model, test it out with a visual [4].

In summary, the training highlights the importance of a well-structured data model as the foundation for effective Power BI reports. It also emphasizes the need for continuous testing and refinement of the data model through data visualization as well as making effective use of Power Query editor, relationships, measures, and DAX calculations to create a reliable and accurate data model.

Power BI Data Cleansing Techniques

The sources provide extensive information on data cleansing within the context of Power BI training. Here’s a detailed discussion of data cleansing based on the sources:

• Importance of Data Cleansing: The training emphasizes that data cleansing is a critical, but often overlooked, part of the report development life cycle. It is essential to ensure the data is accurate and in the optimal format for reporting. Neglecting this step can lead to inaccurate reports and a lack of trust in the data, as even a single incorrect data point can undermine the credibility of an entire report [1].
• Power Query Editor: The primary tool for data cleansing in Power BI is the Power Query Editor, which opens as a separate window [2]. It allows users to connect to various data sources and then clean and shape that data [1].
• The Power Query editor shows a preview of the data (approximately the top 1,000 rows) and allows the user to apply transformations to this sample. This is beneficial for performance when working with large datasets [3, 4].
• The Applied Steps pane in the Power Query Editor is a crucial feature that tracks each transformation step. It allows you to delete or modify any step. Power Query does not have an undo button, so it is important to use this pane to make changes [4].
• The formula bar shows the M code for each transformation step. Although users do not need to know M code to clean data, they can use the formula bar to become familiar with it [4, 5].
• The query settings pane can be turned on or off in the view menu, as can the formula bar [5].
• Data Source Connection: The process begins by connecting to a data source using the “Get Data” button. Power BI offers a variety of connectors, including web, Excel, SQL server, and more [2].
• When connecting to a web source, you might need to provide credentials, but in some cases, an anonymous connection is sufficient [6].
• After connecting to a data source, users have the option to “Load” the data directly into Power BI or “Transform Data” which opens the Power Query Editor [6]. The “Transform Data” option is the correct choice when data cleansing is needed [3, 6].
• Common Data Cleansing Tasks:Renaming Queries: It’s a best practice to rename queries to make them more descriptive. Queries can be renamed in the query settings pane or by double clicking in the queries pane [7].
• Data Type Modifications: Columns have data types (e.g., text, number, date). The training emphasizes the need to verify and change the data types when necessary using the column header or the ribbon [7].
• Handling Null and Blank Values: Null values represent the absence of a value, while blank values are essentially empty spaces [8]. It is important to replace blank values with null values, because it can cause problems with filtering and slicing data in reports [9].
• Filling Down Values: The “Fill Down” function is used to propagate non-blank values down through a column, but it requires that blanks first be replaced by nulls. Sorting is important when using fill down to make sure values are replicated correctly. This can be helpful when working with ragged hierarchical data [9, 10].
• Merging Columns: The training shows how to merge data from multiple columns into a single column using the “column from examples” function [11].
• Removing Unnecessary Data: Unnecessary data should be removed to make the model more efficient and accurate [12].
• Applying Changes:Once data cleansing is complete, the user can “Close & Apply” to load the cleaned data into the Power BI data model [11, 12]. This action applies all the transformation steps to the entire dataset, not just the sample that is shown in Power Query [12].
• The data will then show in the data pane on the right of the screen where you can begin to build your visuals. Data cleansing can also be done after data has been loaded [12].
• Column order in Power Query does not affect column order in the Power BI data model [11, 12].

In summary, the training emphasizes that data cleansing is a vital first step when building Power BI reports. It involves using the Power Query Editor to connect to data sources, clean and shape the data, and load it into Power BI’s data model to ensure reliable data that is ready for analysis and reporting.

Power BI Data Sharing and Collaboration

The sources provide detailed information about data sharing within the context of Power BI training. Here’s a comprehensive discussion of data sharing based on the sources:

• Importance of Data Sharing: The training emphasizes that the full potential of Power BI is realized when reports are shared and collaborated on with others using the Power BI service (powerbi.com) [1]. The Power BI service is a web version of Power BI and is the place where you can share, collaborate, set security, and schedule data refreshes [1, 2].
• Publishing to the Power BI Service:The process of data sharing begins by publishing a completed Power BI report from the desktop application to the Power BI service by using the “Publish” button [1, 3]. When a report is published, both the report and its underlying data (semantic model) are uploaded to the cloud service [2].
• Workspaces are used to organize reports and datasets in the Power BI service [2]. Workspaces help to keep items contained together and make them easier to find [4]. There are two main types of workspaces [2]:
• My workspace: This is a personal space for individual users to practice and track reports. Sharing is not possible from “My Workspace” [4, 5].
• Standard workspaces: These are designed for sharing and collaboration [4, 5].
• During publishing, the user is prompted to save the report. The page that is active when the save button is clicked will be the first page of the report in the Power BI service [3].
• After publishing, the Power BI service offers a link directly to the published report and an option to view quick insights [6].
• Quick insights are automatically generated visualizations that may be useful to include in reports or dashboards [6]. These insights can be pinned to a dashboard [6, 7].
• If a user wants to use a quick insight in a report, they will need to recreate the visual within Power BI desktop and publish the report with that visual [6].
• Licensing: There are two main license levels that determine features in the Power BI service [2].
• Power BI Pro is a lower-level license [2].
• Power BI Premium is a higher-level license with more features [2].
• Fabric is the new name for the premium license [4].
• Sharing Reports:Reports can be shared with individuals, groups, or an entire organization [5, 8].
• When sharing, you can allow users to share the report and build new content [5].
• Reports can be shared through direct access, a copied link, or through Microsoft Teams [5].
• Sharing with groups can simplify the process of giving access to many people [5]. Groups are based on Azure Active Directory (now called Entra ID) [5].
• Data Refresh:Data refresh ensures that reports reflect the latest data by updating the data sources [1, 9].
• Data refresh is set up at the semantic model level, which means that data is updated for all reports built off the same model [9].
• The user can set up a refresh schedule on a daily or weekly basis [10].
• The frequency of the refresh can be determined by how often the data source is updated [10].
• If data is coming from the web, the refresh process is simple, if data is coming from the desktop, a gateway is required [10, 11].
• The number of refreshes per day depends on the type of license. Pro licenses are limited to eight refreshes per day while higher-level licenses allow 48 refreshes per day [10].
• When a data refresh breaks, the user will get a notification by email so that they can fix the issue [11].
• Dashboards: Dashboards are different than reports [7].
• Dashboards allow users to combine data from multiple reports into a single view [7].
• Dashboards are a higher level view than a report, and can be useful for executives who want a quick overview of important data from different sources [7].
• Other Sharing Options: There are other ways to share, such as sharing with Power BI apps and sharing at the workspace level [9].
• Editing in the Power BI Service:Users can make quick edits directly in the Power BI service, though it is generally preferred to do complex editing in Power BI Desktop [8, 12].
• When a report is edited in the service and then the user wants to go back into the desktop to work with the data model, the PBX file must be downloaded from the Power BI Service [8].
• Export Options: In addition to sharing, users can export data to Excel, or export to PowerPoint for presentations [8].
• Collaboration: The Power BI service is designed for collaborative work [1, 5].

In summary, the training emphasizes that sharing and collaboration are crucial for leveraging Power BI’s capabilities. This is facilitated by publishing to the Power BI service, managing workspaces, setting up data refreshes, and sharing reports with others. Understanding these processes ensures that data insights are accessible and up to date.

By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

Affiliate Disclosure: This blog may contain affiliate links, which means I may earn a small commission if you click on the link and make a purchase. This comes at no additional cost to you. I only recommend products or services that I believe will add value to my readers. Your support helps keep this blog running and allows me to continue providing you with quality content. Thank you for your support!