Complete DAX Bootcamp for Power BI from Beginner to Advanced

Install Power BI desktop and download the course datasets. Import, clean, and transform the data in Power Query, then load it into Power BI and build a standalone date table.

Connect to csv data and other data sources in Power BI via Get data, then use Power Query to transform and clean datasets like customer data, product categories, and sales.

Create a standalone date table in Power Query to support yearly, quarterly, and weekly visuals by adding year, month, quarter, and week columns and linking to sales and customer data.

Transform data by disabling load for 2016 and 2017 to remove those years from the report, then apply changes to complete the data transformation.

Explore data model fundamentals by connecting related tables, such as customers, products, and sales, and define relationships using key columns to retrieve customer and product details.

Explore how to distinguish dimension and fact tables by their primary keys, records, and data changes, using customer and product examples and a banking transaction scenario to illustrate relationships.

Explore building a Power BI data model by creating relationships, distinguishing dimension and fact tables, marking a date table, and configuring filter flow and cardinality for a snowflake schema.

Explore what DAX is, how the DAX engine works, and distinguish measure, calculated column, and calculated table, including implicit versus explicit measure.

Discover DAX, a data analysis expression formula language in Power BI, to calculate sales and profit, top customers, rolling averages, and to create measures, calculated columns, and calculated tables.

Discover how to create calculated columns and calculated tables in Power BI using DAX, adding new columns and tables to the data model and viewing them in table view.

Learn to create a measure that sums product cost in Power BI, and compare calculated columns with measures, including when to use each in visuals like cards, tables, and slicers.

Explore implicit and explicit measures in Power BI by showing how a product cost auto-sums, then contrast DAX-based explicit measures with automatically generated ones.

Explore aggregation functions like sum, min, max, average, and count, and how they yield scalar values in a Power BI data model with fact and calendar tables.

Learn how the DAX min function returns the smallest value from a column or two expressions in Power BI, and create measures like minimum product cost.

Explore the max function to identify the largest value in a column or between two scalar expressions, and create a measure like max product cost for a card.

Apply Dax functions to calculate total returns quantity, minimum product price, and maximum product price using the returns and product tables.

Create measures for total return quantity, min product price, and max product price using the sum, min, and max functions. Display each metric on a card with consistent formatting.

Learn how the average function computes the arithmetic mean of a column in DAX, with salary and product cost examples, and create a measure for average product cost.

Learn to calculate averages with DAX across three scenarios: average product price from the product table, average order quantity from sales, and average return quantity from returns.

Create a DAX measure to compute average values across product price, order quantity, and return quantity using the average function; demonstrate aggregation on sales and returns tables.

Count function tallies non-blank rows in a column and ignores duplicates. Create a measure to count customers in the customer table, showing 18,448 total customers.

Learn how the distinct count function counts unique values in a column, and build a total customers in sales measure to show buyers by removing duplicates.

Learn how count rows counts the number of rows in a specified table using the table name, with examples using the territories table and creating a measure total territories.

Tackle the assignment by using count, distinctcount, and countrows to determine how many products are available, ordered, and returned across the products, sales, and returns tables.

Apply count and distinct count to build measures for total products, products in sales, and returned products. Use dimension and returns tables to reveal 293 total, 130 sold, 124 returned.

Explore how filter context shapes measures by calculating total product cost across subcategories and categories, using slicers and the data model to reveal context-driven totals.

The calculate function evaluates an expression in a modified filter context, where expression can be a measure or a sum, overriding category filters to compute total product cost by category.

Explore row context and context transition in dax by creating calculated columns and measures that switch from row to filter context with calculate, showing how total product cost propagates.

Master DAX calculations by building a table of category name and total product cost. Then create measures for accessories and components costs, and a final difference measure to compare them.

Apply calculate to compute total product cost by category, create measures for accessories and components, and compare them with a diff measure, then format a matrix visualization.

Variables store expressions inside a measure, act like constants in context, and can be recalled to return a final result, improving performance, readability, and debugging.

Learn to create and organize a standalone measure table, move all measures into a key measures table, and use folders like 01. basics to keep Power BI DAX measures tidy.

Learn how to format DAX measures for readability using indentation, spacing, and proper line breaks, with variables for component and accessory costs.

Apply sumx in dax to calculate revenue by evaluating each row, multiplying sold price by quantity, and summing the results with related to bring product prices in Power BI.

Create a total cost measure by multiplying order quantity by product cost using a related function. Sum this value and pull the total cost into the visualization.

Create a total cost measure with sumx by iterating over the sales fact table, multiply order quantity by product cost from the product table, and visualize total costs by categories.

Learn how to use the MINX function to compute the minimum revenue across rows by evaluating price times quantity, with or without filter context, using measures versus calculated columns.

Create two measures: calculate total minimum cost by referencing the product cost and quantity, then derive minimum revenue by referencing the total cost measure.

Apply the maxx function in dax to evaluate an expression across rows and within filter context, using price multiplied with quantity to identify maximum revenue.

Compute the total maximum cost by iterating the fact table and multiplying the product cost by the order quantity, using related function or the total cost measure.

Create a total maximum cost measure in the key measure table using Max's formula, multiplying product cost by order quantity via the related function, and validate with the table visualization.

Use the averagex function to compute average revenue by iterating over each row, evaluating price multiplied by quantity, and applying filter contexts by category.

Learn to compute the average total cost per category with AVERAGEX by two methods: multiplying product cost by order quantity, or using a total cost expression.

Apply the DAX context function to count revenues by iterating rows in the fact sales table, calculating revenue as price times quantity with the related function to get product price.

Create a DAX measure to count the total cost, following the same approach used for total revenue count, and refer to the next video for help.

Explore solutions with iterators by building measures for total, minimum, maximum, and average return price across product categories using related, calculate, and returns data in Power BI.

Discover how DAX filter functions—filter, all, all except, and all selected—return tables and enable scalar calculations, contrasting with sum, min, max, and sumx/minx/maxx in Power BI.

Learn how the dax filter function acts as an iterator that returns a table and enables precise filtering with calculate for regions, categories, and amounts.

Create three continent-based total revenue measures by subcategory using calculate and filter, compare with actual revenue, then build max and min revenue measures for North America subcategories.

Learn to use DAX filter expressions on the territories table to compute continent revenues in Power BI, create measures for North America, Europe, Pacific, and derive max and min values.

Use the all except function to remove all filters on a table except the specified columns, and apply it as a calculate modifier.

Discover the allselected function in DAX, which removes filter context on selected tables or columns while preserving external slicer filters, yielding the total revenue 24.91 million across categories and subcategories.

Master DAX concepts all, allexcept, and allselected in a two-scenario assignment that replicates total revenue across countries and customers and counts products by country and customer.

Learn to build DAX measures that manage filter context with ALL, ALLEXCEPT, and ALLSELECTED, calculating revenue and country-specific product counts across a Power BI model.

Learn to build DAX measures in Power BI to calculate total return price and total return quantity across United States, Canada, and Australia using dim territories and fact returns table.

Explore information functions in DAX and how a cell or row argument yields true or false for conditions like is blank or in scope; selected value is a filter function.

Learn the selected value function in DAX, which returns the value when a filter yields a single value and an alternate result otherwise, with cases in customers, regions, and months.

Understand how hasonevalue returns true when a column's context is filtered to a distinct value, compare it with selectedvalue, and apply to continent, country, and region hierarchies in Power BI.

Explore the DAX isblank function to check whether values are blank, returning true or false, with syntax and practical examples using revenue and a total return products measure.

Demonstrate how to use DAX ISEVEN and ISODD to determine if numbers are even or odd in Power BI, with examples on revenue and total return products, including blank values.

Explore how the ISTEXT function checks whether a value is text, returning true or false, with a calculated column example in the territories table (region vs sales territory key).

Master the containsstring function in DAX to determine if one string contains another. Build measures using selected value to respect filter context, and test with first names and emails.

Learn how direct filters on a table or column are detected, with practical examples using the customer.gender column, the first name context, and slicers and visuals.

Explore the isinscope function and how it checks if a column sits at a hierarchy level, returning true or false for category, subcategory, year, and month.

Apply information functions in DAX to retrieve email addresses from the customer table by occupation hierarchy, handle single/multiple values, verify scope, and test for 13 in emails.

Develop DAX information functions to create measures for email selection with selected value, check scope with assign scope, and detect 13 in email text using content string and selector value.

Explore how logical functions in Power BI evaluate expressions to return true or false, using if, and, not, and coalesce. Organize measures in a folder and create a logical page.

Learn the DAX if function with logical tests, true and false results, and nested if logic to categorize total revenue into low, medium, and high with blank handling.

Learn how the not function in DAX inverts results, flipping true to false (and vice versa) in a revenue example using if and or logic in Power BI.

Explore the switch function in DAX to evaluate an expression against values, return results, replace nested ifs, using selected value for subcategory names and low, medium, and high revenue.

Complete a DAX assignment: build year-month hierarchies in a matrix, classify return products as low/medium/high, map months to quarters, and implement a revenue all selected measure for category and subcategory.

Create DAX measures to map month numbers to names, display year and month hierarchies in a matrix, and derive quarters with switch and if.