Microsoft Power BI Desktop : The Practical 2026 Bootcamp!

Learn to download Power BI Desktop on Windows, with Mac users using Parallels, install via the Microsoft Store in minutes, and begin creating visualizations and dashboards.

Learn how to build an ETL pipeline in Power BI by extracting data from sources, transforming it to a clean usable form, and loading into a data model for visualization.

Connect to data sources with Power BI connectors, extract and transform data using Power Query, and load clean data into a data model for visualizations.

Explore the Power BI desktop interface, including the ribbon and the three core sections—get data, transform, and visualize—and how report, table, and model views support building and sharing dashboards.

Discover how to connect Power BI to diverse data sources using built-in connectors—from Excel and CSV files to databases and cloud services—and import, transform, and visualize data without coding.

Connect to the AdventureWorks CSV in Power BI Desktop, preview the comma-delimited data, transform with Power Query, and apply to load into the data model for visualizations.

Explore the Power Query interface, apply step-by-step data transformations from CSV input to promoted headers and type changes, with auto-generated M code and diagnostic insights.

Discover how to promote headers, rename columns, and assign data types in Power Query Editor, with automatic M code generation for date, text, and whole numbers.

Learn data profiling with Power BI column quality to identify missing and empty values, clean data by replacing blanks with unknown, and profile on the entire data set.

Review column distribution to count distinct and unique values and spot duplicates in email. Use column profile insights, including min and max, to guide data cleaning and naming conventions.

Learn to clean and transform data in Power BI by dropping or keeping columns and rows, removing duplicates, blanks, and errors, and naming queries while sorting data before loading.

Explore splitting a power query column using characters, positions, non digit to digit, and delimiters, with examples like order numbers and email domains.

Master the Power Query Editor in Power BI Desktop, including split column by delimiter, data type detection, and formatting to capitalize words and trim data for analysis.

Learn to merge multiple columns in Power Query by selecting with shift, choosing a separator, and naming full name column; see how order and M code like text.upper affect results.

Explore how the add column tab creates a full name column from prefix, first name, and last name, while preserving originals; contrast with the transform tab that modifies existing columns.

Explore the extract tool in Power BI to pull first or last characters, ranges, and text before or after delimiters, and practice merging and reordering columns for clean data.

Create a conditional column in Power BI Desktop to categorize annual income as high salary or low salary and output home owner or non home owner, including unknown handling.

Learn to duplicate columns and add index columns in Power BI desktop, creating copies and sequential or custom starting numbers for rows.

Master date transformations in Power Query to create new date columns, extract year, month, day, quarter, and days in month for time-series insights and curated data visuals.

Explore numerical tools in Power BI, including replacing values, converting to numbers, sine, cosine, tangent, rounding, and basic information checks on numeric columns.

Explore statistics in Power BI Desktop to count rows and distinct values, and use column distribution to compare full data with top 1000 profiling.

Combine multiple similarly structured tables by appending queries in Power BI to form one consolidated sales table for 2015–2017, using append as new to keep originals intact.

Learn how group by creates a pivot-table like summary by aggregating values across categories, and apply it in Power BI’s Power Query to compute average annual income by education level.

Update data sources in Power BI when a file moves by using data source settings to change the file path, or restore the original location and refresh the preview.

Connect to a folder in Power Query to automatically append all CSV or Excel files, enabling seamless ongoing data ingestion in Power BI desktop as new files arrive.

Push transformed data from the Power Query Editor into the Power BI data model, then create visualizations from curated tables in report view.

Enable this technique by disabling load for a transformation in the query editor, preserving its logic without pushing to the Power BI data model, then re-enable when visuals need it.

Learn how normalization organizes data into separate tables and establishes relationships to reduce duplication, prevent inconsistencies, and improve data manageability in Power BI data models.

Understand how primary keys provide a unique identifier for every row, prevent duplicates, and enable precise retrieval, with foreign keys supporting table normalization.

Discover how foreign keys link normalized author and book tables through a parent-child relationship, enforce valid references, and enable cross-table lookups.

Explore fact and dimension tables and their roles in data warehousing. Learn how primary keys and foreign keys connect the tables to answer business questions with aggregated numeric data.

Explore star schema and snowflake schema in organizing fact and dimension tables, using primary key and foreign key relationships to connect dimensions to the fact table.

Snowflake schema extends the star schema by forming a hierarchy of dimension tables, reducing duplication but increasing joins and query complexity; use it only when necessary.

Learn how to join multiple tables into a single unified table by combining normalized data through relationships, using inner, left, right, and outer joins with clear output rules.

Learn to perform inner, left, right, and full outer joins in Power BI by merging queries between two tables, expanding results, and validating outputs against expectations.

Explore the three cardinalities—one-to-one, one-to-many, and many-to-many—and learn how they shape relationships in Power BI, with practical examples and guidance on star schema, dimension tables, and the fact table.

Configure and build a Power BI data model by loading multiple CSVs, renaming tables, and manually defining relationships, then append 2015–2017 sales into a single fact table for analysis.

Establish a Power BI data model by manually creating one-to-many relationships across categories, subcategories, products, and sales. Clean duplicates to avoid many-to-many keys and apply star and snowflake schema concepts.

Enhance the data model by joining the sales and products tables using an inner join on the product key, adding product price and cost, and creating a calculated profit field.

Save your Power BI workbook as a .pbix, enable all preview features in options, restart, and prepare to create your first visualization using the data model's tables and fields.

Create your first matrix in Power BI Desktop, populate rows with product color and values from the sales fact table, and format with background, borders, title, and presets.

Create a Power BI bar chart by dragging fields to the y and x axes, then format titles, axes, data labels, colors, and conditional formatting to show revenue by color.

Explore bar chart variations in Power BI, switching between row and column orientations, and use clustered and stacked column charts with legends, benchmarks, and customized tooltips for clearer insights.

Create a line chart to show revenue trends over time with a date hierarchy, drill down to year, quarter, and month, and add data labels, a trend line, forecast.

Switch to an area chart that fills to x axis with date and price, drill down to level, rename axes, and add zoom, markers, data labels, and a trend line.

Learn to use the format painter in Power BI to copy formatting between visualizations, duplicating color schemes, decimal places, lines and dots, for faster KPI dashboards.

Create a line and stacked column chart to display revenue by product color with a profit line on a single visual, using a secondary y axis if needed.

Learn to create and format key performance indicator cards in Microsoft Power BI Desktop, including setting up total revenue KPIs, adjusting formatting, and highlighting top metrics.

Learn to rapidly add multiple cards in Power BI Desktop, apply consistent formatting with Format Painter, rename visuals, and explore drill-down effects on revenue, costs, and profit.

Create and format a pie chart in Power BI, add a legend and sales category data, and learn when to switch to a bar chart for clarity.

Learn to create a donut chart in Power BI, compare it with a pie chart, and customize inner radius, rotation, colors, and legend for a tailored visual.

Explore Power BI's conditional formatting in a matrix, applying background colors, data bars, font color, and icons to revenue and profit, with custom rules and icon layouts.

Explore how to build a funnel chart in Power BI desktop by setting category and value fields, display profits, and interpret the 100% first value with relative percentages.

Explore tree maps in Power BI, break down profits by category and subcategory, and use legends, data labels, and color customization to reveal the largest values from top-left to bottom-right.

Create and customize a gauge chart in Power BI Desktop to compare total revenue against a target, using a randomly generated target revenue column with randbetween.

Create a KPI in Power BI with a trending date axis, drill down to daily revenue, and compare to daily targets to visualize on track vs off track performance.

Master Power BI filtering across visuals, pages, and all pages, using basic and advanced options, top-n selections, and numeric versus discrete field filters.

Explore how slicers in Power BI Desktop empower users to filter data visually, switch between categories, and filter multiple visuals through tiles, dropdowns, and single or multi-select options.

Explore map visuals in Power BI by enabling ArcGIS, using bubble maps and field maps, with legend colors, profit-based bubble sizing, and continent slicers.