Complete Microsoft Power BI course

Explore module two basics of Power BI, focusing on the project blueprint, the Power BI interface, and essential settings and configurations for successful projects.

Identify diverse data sources and load them through Power Query Editor by extracting, transforming, and loading, then build a data model with relationships to create an interactive Power BI report.

Explore the Power BI interface, navigating the three panes—report, data, and model—and use Power Query editor and transform data workflows to shape tables, relationships, and visualizations.

Prepare Power BI desktop by disabling current-file features such as type detection, relationships, and time intelligence, set English India regional settings, and save a blank template.

Explore module three of the Microsoft Power BI course to learn data preparation, cleaning raw data, handling missing values and anomalies, and applying basic to advanced data shaping techniques.

Explore data preparation in Power Query Editor, covering extraction, transformation, loading, and handling duplicates, bad or missing values, unnecessary columns, and joining tables from multiple sources for visualization.

Connect Power BI to CSV and Excel data sources using Get Data, preview in Power Query Editor, and refresh to update the model without changing the source data.

Learn practical Power Query row operations in Power BI, turning the first row into headers, counting rows, and removing top, bottom, duplicate, and blank rows across datasets.

Filter data in the Power Query Editor to load only United States rows and remove no data before loading to the data model.

Learn to preview data quality in Power Query before loading to the model, using data quality views, column profile and distribution, and first/last thousand row checks.

Learn how to use append, transpose, and replace features in Power BI to merge tables, reshape data, and standardize values for cross-source analysis.

Learn how to use append queries in Power Query to combine winter and summer games tables into one dataset, while preserving source connections.

Learn to fix append query errors by aligning column name and data type between winter and summer datasets, then explore the transform tab's transpose feature and its impact on queries.

Learn how to clean data in Power BI by replacing values to standardize inconsistent team names, such as Australia, Australia one, and Australia two, for accurate visualizations.

Master advanced Power Query features like unpivoting and pivoting, manual data type assignment, delimiter-based extraction, splitting, and group by with sum or average to prep data for visualization.

Assign data types in Power Query editor, convert event id and age to whole numbers, apply sorting, and load three tables into the Power BI data model while resolving errors.

Apply unpivot and pivot transformations in Power Query to reshape data, demonstrated on a small table, and note that these advanced features may not be required in every project.

Learn advanced power query techniques to extract and split data in Power BI, isolating the year from a combined games column using before/after delimiter methods and space-based splitting.

Explore group by in the Power Query editor to produce crisp, visualization-ready tables, computing minimum age by country and yearly counts by gender.

Explore module four of the Microsoft Power BI course as you move beyond data layer to a data model, converting large data and applying it to the athletes database project.

Convert your data layer into a data model to handle large data efficiently and derive better insights. Connect multiple dimension tables through relationships to form a scalable data model.

Explore star schema data model theory with a central fact table and surrounding dimension tables for efficient querying. Identify primary and foreign keys that connect dimensions to the fact table.

Fit a star schema into an existing project by turning columns into player, game, and country dimensions with a central fact table linked by keys.

Learn how to convert a data layer into a data model in Power BI using reference and duplicate techniques to create table replicas, with linked versus independent outputs.

Discover how the merge query in Power BI combines two tables on the student ID to fetch names and add a name column, with upcoming types of merging to learn.

Explore merge queries in Power BI to perform left, right, inner, and full outer joins using student IDs as examples, and prep for a star schema data model.

Create a player dimension in Power BI with name, gender, age, and team; remove duplicates and add a unique player ID, then outline game and region dimensions for star schema.

Create a game dimension table by selecting season, city, sport, and event, deduplicating rows, and adding a unique game id to prepare regional and fact tables in a star schema.

Duplicate the main table to create region dim, merge with a regions CSV via a left outer join to import region names, and add an entry ID.

Create a central fact table in a star schema by merging athletes fact with player, game, and region dimensions, selecting ids and the medal for loading into the data model.

Learn to organize queries in the Power Query editor by grouping source and final tables, enabling load for only selected tables into the Power BI data model.

In module five of the Complete Microsoft Power BI course, learn how to model data by establishing relationships between dim and fact tables and configuring settings for reliable reports.

Load four tables—athletes, facts, game name, and regional team—into the data model and establish relationships for a bar chart of medal counts by country.

Learn to manage relationships in Power BI using the manage relationships dialog, set correct cardinality (many-to-one, one-to-many, etc.), understand cross filter direction, and activate or deactivate relationships.

Explore how cardinality connects tables in Power BI desktop, focusing on one-to-one, one-to-many, and many-to-one relationships. See practical examples with student and marks tables to determine connections.

Learn to model Power BI data using one-to-one and many-to-many relationships by linking the student, address, subjects, and games tables via student IDs and apply cardinality concepts.

Explore cross filter direction and active vs inactive relationships in Power BI data model. Learn how cardinality, cross filter, and active status affect filter propagation across tables.

Apply relationships to connect four tables, validate many-to-one and one-to-many cardinalities, set cross filter direction to both, and create an active star schema data model for Power BI visuals.

Explore the two languages in Power BI, M language and DAX, and learn basic to advanced DAX functions for exploratory data analysis and data manipulation before building an interactive dashboard.

Add a conditional column in the Power Query editor using the M language to count gold medals by returning 1 when medal equals gold and 0 otherwise.

Learn to create a conditional column using Dax in Power BI desktop, compare Dax with M language, and build a gold medal count by Dax using the if function.

Explore the roles of M language and DAX. M handles data transformation in Power Query Editor, while DAX performs analysis and calculations in Power BI Desktop.

Master dax basics in the complete microsoft power bi course by learning to create calculated columns and measures using the if function and proper syntax.

Learn dax operators, including arithmetic, comparison, concatenation, and logical operators, with practical examples like calculating total medals (gold, silver, bronze) and building concatenated region and NOC values.

Learn how to use DAX nested if function to bucket ages in the player team table into less than 20, 20 to 40, 40 to 50, and 50 plus.

Explore the related function in dax to fetch the player name from the player dimension into the athletes fact table using the model relationship.

Pull data from multiple tables and aggregate it with the DAX calculate and filter functions. Compute medals by country and filter for gold medals in the athletes fact table.

Create a new measure in Power BI using a DAX expression to calculate aggregated values. Compare with a calculated column: measures yield total medals in visuals, while columns compute row-wise.

Learn to hide sensitive and duplicate fields from the Power BI report view, exposing only essential columns while keeping data in the model and renaming fields for clearer visuals.

Explore visualization practices in Power BI, using colors, themes, and designs to turn a prepared star schema with DAX measures into compelling report visuals.

learn how to design and explore an interactive Olympics history dashboard in Power BI, using region filters, year slicers, and sport-specific medal tallies to tell data-driven stories.

Learn how to use the Power BI report view to design a dashboard, configuring the canvas with fields, visualizations, and filters, and setting background color or image.

Arrange shapes and images in a Power BI dashboard by inserting a white rectangle, adjusting shape options, and layering images using bring to front or send to back.

Learn how to create and customize card visuals in Power BI to display medal counts, including total medals and counts by gold, silver, and bronze, with filters and formatting.

Create a trend line in Power BI by building a multi-line time-series chart with year on the x-axis and bronze, silver, and gold medals, refined with legends and filters.

Create a tabular view in Power BI by listing top three athletes with their country and medal count, using winner and top N filters and table formatting.

Create a bar chart to display sport-wise medal counts using a total medal measure and top five filtering, with clear labeling and formatting for a polished Power BI dashboard.

Build a clustered column chart of top five countries by total medals with bronze, gold, and silver, and a stacked bar chart of gold medals by gender.

Create a donut chart to show proportional gold medals by age group, display absolute values, customize legend colors, and set detail labels for your Power BI dashboard.

Create and configure a slicer in a Power BI dashboard, choosing display as list, tile, or dropdown. Use season, region, sports, and year to dynamically update visuals.

Explore module eight overview and preview advanced visualizations from the marketplace to enrich Power BI dashboards, building on data connections, modeling, and visualization techniques learned so far.

Explore AppSource to add new visuals to Power BI and learn to import or get more visuals, featuring zebra cards, spider charts, and word clouds for dashboards.

Add a text filter visual in a Power BI report to enable live search by player and region, import from the marketplace, and integrate with the dashboard.

Create four data stories in a Power BI dashboard using the data story visual, highlighting total athletes, top medal country, leading medalist, and youngest gold medalist.

Module nine guides final touch-ups to a feature-rich Power BI dashboard—arranging shapes, sizes, and colors for launch after mastering connections, data models, star schema, DAX, and Power Query.

Master size and placement touch ups in a Power BI dashboard by aligning elements on a 720 by 1280 canvas and setting widths and gaps for cards, slicers, and images.

Apply Olympic color codes to the Power BI dashboard, aligning visuals, headings, and slicers with brand colors. Remove shadows, adjust borders, and group key metrics for a live-ready, cohesive design.

Enable the mobile layout in the Power BI desktop view tab and convert the dashboard into a mobile friendly view, keeping the desktop version untouched and visuals adjustable for mobile.