The Dax training course was very well organized and the instructor was very knowledgeable. I would recommend this course to anyone interested in learning about Dax.

DAX syntax is a language used to create calculations in data models. It is similar to the syntax used by other spreadsheet programs and it can be used to perform mathematical operations on data cells. DAX syntax includes functions, operators, and values that can be used to create formulas.

Operators are symbols that represent actions that can be performed on data cells. Functions are predefined formulas that can be used to perform calculations. Values are the actual data points that are being manipulated by the operators and functions. 

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DAX syntax uses a combination of upper and lower case letters. Functions must be written in all lowercase letters while operators and values can be written in either upper or lowercase letters.

The following is an example of a DAX formula:


 This formula would calculate the sum of all sales amounts in a table called Sales.

Creating Calculations with DAX

When it comes to creating calculations with DAX, there are a few things you need to keep in mind. First, DAX is not a programming language, so you can’t just write a calculation and expect it to work. You need to use the DAX functions that are available to you.

Second, DAX is case-sensitive, so make sure you use the proper case when writing your calculations.

Third, make sure your calculation makes sense. This may seem like common sense, but it’s easy to get caught up in the syntax of DAX and lose sight of what you’re trying to accomplish with your calculation.

Fourth, don’t forget that you can always reference other columns in your table when writing a DAX calculation. This can be helpful if you want to create a calculated column that is based on data from another column.

Finally, keep in mind that there are many different ways to write the same calculation in DAX. So if you’re stuck on how to write a particular calculation, try searching for examples online or reaching out to someone who is more experienced with DAX.

Advanced DAX Calculations

DAX provides various time intelligence functions that enable you to perform calculations using dates and times. These functions can be used to calculate things like the year-to-date sales, the average sales per day, or the total sales for a given period.

DAX also provides a set of statistical functions that enable you to perform complex calculations on your data. These functions can be used to calculate things like the standard deviation of a set of data, the median value, or the mode.

In addition to these built-in functions, DAX also provides a set of operators that enable you to create custom calculations. These operators can be used to create complex formulas that perform calculations on your data.

In this article, we will take a look at some of the more advanced DAX calculations that you can perform on your data. We will also look at how you can use these calculations to create custom reports and dashboards in Power BI.

Using DAX to Analyze Data

When it comes to data analysis, DAX is a powerful tool that can be used to help you make better decisions. This article will explore some of the ways that you can use DAX to analyze your data.

One of the great things about DAX is that it can be used to calculate new columns based on existing data. This means that you can easily add new perspectives to your data without having to go back and change the original data set. For example, if you want to see how customers have interacted with your website over time, you could create a column that shows the number of page views per customer per day.

Another useful way to use DAX is to filter your data. This can be helpful when you want to focus on a specific subset of your data. For example, if you only want to see orders from customers in a certain region, you could filter for that region using DAX. Filtering can also be used to remove outliers from your data set, which can be helpful when trying to identify trends.

Finally, DAX can also be used to create visualizations of your data. This can help understand complex patterns or relationships within your data set.