[NEW] PL-300: Microsoft Power BI Data Analyst Associate 2026

Install Power BI Desktop on Windows to create visualizations, dashboards, and massaging our data via the Microsoft Store download process.

Explore the power BI desktop interface, including the ribbon and core panes. Learn how data flows from connector to model and report views for dashboards and collaboration.

Enable all preview features in Power BI Desktop to ensure a consistent interface across versions; enable on object interaction and restart the application to apply changes.

Explore how ETL (extract, transform, load) pulls data from diverse sources, cleans and blends it, and loads it into a Power BI data model to support accurate visualizations.

Learn how to perform ETL in Power BI using connectors, Power Query, and data models to create visualizations from clean, trusted data.

Explore the Power BI lifecycle, connecting to diverse data sources with connectors, transforming data in Power Query, building a data model, and creating visualizations in report view.

Learn to connect Power BI to a CSV file, preview data, choose delimiters, and use Power Query to transform and load data into the data model for visualization.

Set the regional settings in Power BI to English Canada for non-North America users to ensure correct interpretation of dates and numbers in imports; refresh the preview to remove errors.

Navigate the Power Query interface in Power BI, transforming CSV data with applied steps—from promoting headers and changing data types to adding columns and loading to the data model.

Learn to promote headers and set data types in Power BI via Power Query Editor, handling CSV inputs with M code and renaming columns.

Explore data profiling and column quality in Power BI, identify empty values, and clean data with replace and filter steps to ensure 100% valid columns.

Apply column distribution and column profile insights to identify distinct and unique values, detect duplicates such as emails, and guide data cleaning and exploratory data analysis in Power BI.

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

Explore splitting a column into multiple parts in Power BI using split column methods: by characters, by delimiter, and by positions, including domain extraction from emails.

Explore the Power Query Editor's transform tools to rename and split columns, auto-detect data types, and apply formatting—lowercase, capitalize each word, and trim or clean to tidy data efficiently.

Select multiple columns with shift, then merge to create a full name from prefix, first name, and last name; order and separator determine the result, with M code like text.upper.

Use the add column tab to create a new full name from prefix, first name, and last name while preserving originals; compare with transform that updates existing columns.

Master the extract tool in Power BI to pull text ranges from strings, including length, first and last characters, and text before or after delimiters, while reordering and merging columns.

Learn to create conditional columns in Power BI using if-else logic to classify annual income as pay categories and to build a homeowner classifier with Y, N, and unknown outcomes.

Duplicate a column to create a 1-to-1 copy, such as a birth date column, and add an index column to generate a unique identifier starting from 0 or 1.

Explore date transformations in Power Query to derive age, year, start and end of year, days in month, month name, day of week, and quarters for time series analysis.

Explore numerical tools in Power BI data transformation, including replace values, convert to whole numbers, add new columns, and compute sine, cosine, tangent, rounding, absolute value, square root, and division.

Learn to use statistics to count rows and determine distinct values in a column, such as last name, and compare full data set profiling with column distribution for accurate visuals.

Append queries in Power BI to union 2015, 2016, and 2017 sales tables with identical columns into a new single table, preserving originals and delivering a clear appended result.

Group by creates pivot-table style summaries by category, aggregating sales with sum or average; in Power BI, group by education level to compute average annual income, highlighting the granularity loss.

Connect to a folder in Power Query Editor to automatically append all files into one table, streamlining updates when new CSV or Excel files arrive.

Learn how to fix a broken data load when the file moves by using data source settings to change the source in Power BI and refresh the preview.

Learn how to import a JSON file into Power BI using the Power Query Editor, convert unstructured data into a tabular format, and expand records into usable columns.

Sort by first name, replace blanks with unknown, and handle infinities when dividing income by total children. Create a custom column to compute a division using a flexible editor.

Preserve transformation logic in Power BI without pushing data to the data model by disabling load, then re-enable when needed for visuals and reports.

Explore duplicate vs. reference queries in Power BI, learn how to clone queries with identical M code, and how reference links propagate changes while maintaining separate dependencies.

Master common Power Query import errors in Power BI, covering step- and cell-level issues, and learn practical fixes for missing columns, type conversions, and error handling.

Explore pro tips in the Power Query editor. Use the gear icon or double-click a step to change the data source, tweak applied steps, and manage load to improve performance.

Configure Power BI data loading settings to control type detection, headers for unstructured sources, and parallel query loading in Power Query Editor, with file-level overrides and cache management.

After completing all transformations, push the cleaned data into the data model, then switch to report view to create visualizations from the transformed tables.

Learn normalization in Power BI data models by splitting data into author and book tables, establishing relationships, reducing redundancy, and ensuring consistent updates.

Learn how primary keys provide a unique identifier for every row, enforce uniqueness and non-null values, and distinguish records using foreign keys to normalize tables.

Explore how foreign keys create parent-child relationships between tables, enforce primary keys, and prevent invalid data while linking authors to their books through lookups.

Split a big table into a fact table of numeric measures and dimension tables of descriptive data. Link them with foreign keys to support aggregate, context-rich queries.

Explore star schema and snowflake schema by connecting multiple fact and dimension tables with primary and foreign key relationships, and visualize simple model layouts in the Power BI data model.

Explore the snowflake schema as an extension of the star schema, adding hierarchical dimension tables like manufacturer. Recognize the trade-off of reduced data duplication versus increased query complexity from joins.

Learn how to combine two input tables into a single unified table using inner, left, right, and outer joins, guided by primary and foreign keys and practical Excel demonstrations.

Merge two tables in Power BI with merge queries, using inner, left outer, right, and full joins on the ID key. Expand outputs to ensure correct row counts.

Explore cardinality in Power BI, including one-to-one, one-to-many, and many-to-many relationships between tables. See how dimension and fact tables form a star schema and why many-to-many requires cautious, clear understanding.

Configure a Power BI data model by loading CSV files, building dimension tables, creating a unified sales fact via append, and manually defining relationships.

Define and connect data model in Power BI by creating one-to-many relationships among product categories, subcategories, products, customers, territories, and sales, while cleaning keys and validating star and snowflake schemas.

Learn to normalize a denormalized dataset in Power Query by creating fact and dimension tables, planning schema, and building a star or snowflake data model in Power BI.

Explore how Power BI privacy levels—private, organizational, and public—control data joins across sources to prevent leaks. Learn best practices like tagging sources and avoiding private data in web requests.

Set privacy levels for Power BI data sources in data source settings and edit permissions. Choose public, organizational, or private to prevent leakage and manage privacy-check costs.

Connect to a local MySQL database in Power BI Desktop by specifying host and port, previewing tables, and choosing load or transform to create the data model.

Update credentials and data source settings in Power BI to fix authentication failures when database passwords change, including editing credentials, changing sources, and refreshing queries.

Create and manage parameters in Power BI to dynamically filter data, reuse a single rating selector across queries, and rename columns using parameter-driven M code.

Compare direct query and import modes in Power BI, exploring in-memory Vertipaq performance, real-time data access, data freshness, gateway needs, and offline vs online use cases.

Use import mode in Power BI for fast responses and complex data modeling with local cached data; choose direct query for real-time needs and large datasets staying at the source.

Explore composite models in Power BI by blending import and direct query modes, and learn how dual storage mode lets a table switch between them at runtime.

Explore practical use of import, direct query, and dual storage modes in Power BI, building a composite model with sql server data, configuring storage modes, and understanding implications at runtime.

Explore how to configure table properties in Power BI, control load, manage card and data model settings, describe tables, set synonyms, and hide tables to tailor the data model.

Configure field properties in the Power BI data model by renaming fields, adding descriptions and synonyms, organizing with display folders, and setting data types, sorting, and formatting for visuals.

Understand how cross filter direction propagates filters through Power BI relationships to yield accurate visuals. Learn best practices for one-to-many relationships and avoiding bidirectional filtering.

Demonstrate how filter context and relationships affect Power BI visuals by linking product color to profit with a one-to-many relationship, revealing how filter direction and cardinality shape totals.

Explore auto date time and common date tables in Power BI, and understand how hidden back end date tables enable time intelligence and drill down.

Create and mark a custom date table in Power BI desktop to enable time intelligence with a unified, unique, non-null, contiguous calendar. Supports DAX time calculations like year-to-date.

Explore role playing dimensions in Power BI by splitting the date into separate order date and stock date tables, enabling multiple active relationships and precise filter context.

Explore the ready-made semantic model provided by Microsoft, featuring a sales fact table, related dimension tables, and one-to-many relationships, and learn to build visuals and dashboards with a calendar table.

Learn to customize the Power BI canvas for desktop visuals, including canvas size and background, and manage format and filter pane settings for clear reports.

Create your first KPI card in Power BI Desktop, drag the sales amount into the visual, and explore aggregations like sum, average, min, and max with formatting options.

Learn to use format painter to copy formatting across KPIs in Power BI, align visuals, set decimals, and rename labels, while understanding what formatting copies and what it doesn’t.

Create and customize a clustered column chart in Power BI to segment total sales by continent. Learn how to adjust axes, titles, tooltips, colors, borders, and grid lines.

Explore Power BI clustered column and bar charts, enable data labels and currency formatting, add reference lines, and use drill-down hierarchies with legends for insights.

Explore how to build a stacked bar chart in Power BI, break data by channel names, customize legends, and convert to a ribbon chart for clear flow insights.

Create and customize a Power BI line chart to show yearly sales trends using a calendar table, continuous axes, and forecasting with confidence intervals.

Learn to build multi-field line charts in Power BI by adding sales, cost, and quantity over time, and customize legends by continent for clear trend visualization.

combine line and clustered column chart to show sales amount as columns and total cost as a line, using a primary y axis and a secondary y axis. align zeros for the two axes and add a legend to compare yearly totals across continents while clarifying axis mappings.

Learn to combine a line with a stacked column chart in Power BI, displaying 2011 sales by continent alongside a total cost marker and labeled totals.

Convert a line chart into an area chart by turning on shade, adjust transparency, then create a stacked area chart by continent and explore small multiples by year and month.

Explore a stacked area chart variation that stacks sales amount and total cost, removing the return amount to reduce noise, and adjust colors, transparency, and stacking by y-axis or legend.

Explore creating and formatting pie charts in Power BI, including choosing values and legends, breaking down sales by category and channel, and refining labels, colors, and angles.

Explore how to convert a pie chart to a donut chart in Power BI by adjusting the inner radius, while keeping formatting and data values unchanged.

Learn to build a Power BI treemap that shows total cost by channel, with color, category labels, data labels, and customizable tiling and display units.

Explore how to create and format a gauge in Power BI to compare year-to-date sales against targets, using auto or dynamic min, max, and target values.

Explore scatter, bubble, and dot plot charts in Power BI to visualize relationships between numerical values, break down data by geography, and enhance insights with legend, size, and formatting.

Explore building a Power BI table visualization and using relationships across geography, channel, and product tables to break down sales, cost, and quantity by continent, channel, and category.

Learn to build a matrix in Power BI that replicates pivot tables from Excel, using rows, columns, and drill-down hierarchies for continent, channel, category, and subcategory with sales and costs.

Explore applying conditional formatting in Power BI tables and matrices, including background color, font color, data bars, and icons, using gradients and rules with precedence and practical setup.

Explore Power BI conditional formatting for percentage fields, learn to divide by 100 to use numbers, and see how formatting rules override existing colors.

Format - properties tab in Power BI lets you set data formats, default summarisation, and per-visual formatting for fields like sales amount, including currency and decimal places.

Learn to filter data in Power BI desktop using page, all pages, and visual filters with basic, advanced, and top-n techniques, including calendar year filtering.

Publish reports to the Power BI service, share via links or Active Directory groups, and apply filters at visual, page, or report level with reset to default.

Master slicers in Power BI Desktop to enable on-page filtering, with date ranges and multiple layouts such as between, drop-down, tiles, and multi-select with select all.

Explore the new slicers in Power BI, including the button slicer, list slicer, and text slicer, and learn to customize state, layout, and call out values for dynamic filtering.

Sync slicers across pages, filter slicer values, and group slicers with a common name to share filters. Copy and paste to propagate selections and manage visibility with advanced options.

Apply and customize a Power BI report theme to enforce organizational fonts, colors, and visuals across all pages; import JSON themes, export them, and tailor defaults for consistent branding.

Learn how to sort visuals in Power BI, including creating a custom sort with a Power Query column and applying sort by column to order regions, e.g., Canada first.

Customize Power BI tooltips, create custom tooltip pages, and pass filter context to display rich insights on hover.

Learn how paginated reports in Power BI are built with Report Builder for printing long, multi-page invoices; publish to the Power BI service and embed in desktop.

Explore DAX, the Power BI formula language for creating dynamic calculations, measures, and calculated columns and tables, with context-aware analytics like year-to-date and year-over-year insights.

Explore DAX functions in Power BI, using official Microsoft documentation and the DAX dot guide to learn aggregation, statistical, and text functions, plus iterator functions, with syntax, examples, and practice.

Discover creating calculated columns in Power BI desktop with DAX inside the data model from report, table, or model views, and compare with Power Query Editor.

Learn to write Dax string functions in Power BI, including concatenate and ampersand for joining text, exact and find for comparisons, and len for length, with nesting tips.

Explore text functions in Power BI, focusing on the format function to format numbers, dates, and text, and learn left, right, mid, lower, upper, trim, value, substitute in DAX.

Explore the boolean data type and core DAX logical functions—false, true, or, and, not—and learn to construct valid expressions, compare values, and nest functions.

Explore the if and switch functions in DAX, understanding syntax, true/false tests, default blanks, scalar returns, and practical Power BI examples.

Explore how to use the DAX related function to pull the promotion name from the related promotions table based on promotion key, enabling line-item calculations and discount amounts.

Explore how implicit measures auto generate in Power BI when you drag fields, and how explicit measures enable reuse across visuals and centralized logic.

Explore implicit measures in Power BI and compare them with explicit measures, learning how visuals auto-aggregate fields like sales amount, with default summarization and filter context shaping on-the-fly calculations.

Explore explicit measures in Power BI by writing DAX to create reusable, named calculations that enhance visuals and reports, with best practices for centralizing measures.

Create explicit measures in Power BI by centralizing them in a measures table, using DAX to sum sales amount, and compare with implicit measures across visuals.

Learn to create single aggregation measures in DAX, convert calculated columns to measures, and use average, count, counta, count blank, count rows, and distinct count on a geography table.

Create single aggregation measures using max and min functions, and learn how measures interact with the data model, row and filter context, and bidirectional filtering.

Learn to use basic statistical functions in DAX, including median, geometric mean, standard deviation, variance, normal distribution, and rank, and understand how row and filter contexts shape results.

Learn how iterator functions in Power BI enable on-the-fly calculations with the sumx and averagex functions, avoiding stored columns and data model bloat while creating dynamic measures.

Learn how the calculate function in Power BI modifies the filter context to evaluate expressions, control filters like Asia and year, and override slicers for precise calculations.

Master the keepfilters function in DAX to intersect forced filters with the visual filter context, producing Asia 2011 sales without duplicating values across continents.

Apply remove filters and all to control filter flow in Power BI by clearing filters from tables or columns and using keep filters for intersections.

Learn how allselected preserves outside filters while ignoring inside query filters to sum sales by continent name, using Asia, Europe, and North America with year 2011 and slicers.

Explains the use relationship function in DAX to activate a disabled relationship at runtime for a specific calculation, enabling shipping date vs order date analysis within a Power BI model.

Explore creating calculated tables in Power BI with DAX, using filter and values to build virtual and physical tables, and summarize or group by to derive insights.

Learn to build calculated tables in dax by using summarize columns with filters to produce filtered group results, then extend with add columns, generate series, and top n.

Master time intelligence in Power BI by using DAX to compare performance across day, month, quarter, and year. Build and apply a calendar table and essential time intelligence functions.

Learn to compute time intelligence measures in Power BI using year-to-date, month-to-date, week-to-date, and quarter-to-date, with a calendar date key to analyze revenue.

Explore time intelligence in Power BI by using dates in period to build custom running totals and moving windows, combining year-to-date, month-to-date, quarter-to-date, and week-to-date with a calendar table.

Use the date add function to shift dates by intervals, enabling month-over-month analysis and running totals with time intelligence measures and a date table.

Explore semi additive measures in DAX, learn how they differ from fully additive measures, and apply snapshot techniques like first date to show the closing balance.

Create calculation groups in Power BI to centralize logic and reduce explicit measures by applying time intelligence calculations (ytd, mtd, qtd) to the selected measure.

Discover how to create DAX measures with quick measures in Power BI, using base values, filters, year-to-date, year-over-year, and explicit measures for reliable analytics.

Explore how to create visual calculations in Power BI using running totals and moving averages with new visual calculations, quick measures, and GUI-based DAX, without persisting to the data model.

Explore drill down and drill up in Power BI visuals, shifting between year, month name, and quarter levels to reveal deeper sales insights.

Learn to edit and configure interactions between visuals in Power BI Desktop, switching between cross-highlighting and cross-filtering, and propagate drill-down filters across a page.

Create Power BI bookmarks to capture a report’s current state, including filters, slicers, and sort order. Distinguish report level and personal bookmarks to apply views with one click.

Group visuals using the selection pane to control the layer order and visibility, arranging KPIs, slicers, and charts, then align, distribute, and group for consistent layout and accessible tab navigation.

Configure navigation for a report demonstrates how to manage page visibility and user flow in Power BI with buttons, shapes, images, and the page navigator for cohesive data storytelling.

Configure drill through navigation in Power BI to move from a KPI page to a detailed continent page, passing filter context and enabling focused analysis.

Configure Power BI export settings by understanding how to export summarized, underlying, or data with current layout to Excel or CSV, apply export permissions, and respect sensitivity labels.

Design mobile-optimized Power BI reports using the mobile layout view and auto-create mobile layouts, with portrait mode viewing and horizontal mode web view when published to Power BI service.

Power BI's personalize visuals lets each user tailor a report by swapping visualization types, measures, and legends, then save changes with bookmarks or share via links.

Design and configure Power BI reports for accessibility by implementing keyboard navigation, alt text, and screen reader friendly visuals, using focus mode and high-contrast options for inclusive data insights.

Enable automatic page refresh in Power BI to keep visuals current via fixed intervals or change detection on DirectQuery sources, with desktop and premium service considerations.

Learn to use the Performance Analyzer in Power BI to identify bottlenecks across visuals, DAX queries, and rendering times, and compare interactivity presets to optimize end-to-end report performance.

Reduce Power BI data model size by removing unnecessary columns and rows, using vertical and horizontal filtering, to boost refresh speed and query performance.

Group data in Power Query Editor to reduce granularity, use pre-aggregated tables, and balance direct query storage mode with import mode for performance, while optimizing column data types.

Disable power query load for unused tables and disable auto date time. Use direct query storage mode for fact tables in a composite model, with group and summarize for visuals.

Explore how to use the analyze feature in Power BI to uncover why sales decline, explain trends, and compare distributions across continents, channels, and promotions.

Discover how to group and bin data in Power BI Desktop by creating category groups, using include or exclude filters, and persisting a new grouping column for visuals.

Explore binning and clustering in Power BI by turning numeric fields into bins and generating clusters, then visualize with scatter plots and play axis.

Explore four AI visuals in power BI, including smart narrative, Q&A, key influencers, and decomposition tree, and use anomaly detection to identify anomalies with date time values.

Explore forecasting in Power BI with error bars and reference lines, learn to set upper and lower bounds, confidence intervals, and constant lines for interactive time-series analysis.