Book Image

Hands-On Business Intelligence with DAX

By : Ian Horne
Book Image

Hands-On Business Intelligence with DAX

By: Ian Horne

Overview of this book

Data Analysis Expressions (DAX) is known for its ability to increase efficiency by extracting new information from data that is already present in your model. With this book, you’ll learn to use DAX’s functionality and flexibility in the BI and data analytics domains. You’ll start by learning the basics of DAX, along with understanding the importance of good data models, and how to write efficient DAX formulas by using variables and formatting styles. You’ll then explore how DAX queries work with the help of examples. The book will guide you through optimizing the BI workflow by writing powerful DAX queries. Next, you’ll learn to manipulate and load data of varying complexity within Microsoft products such as Power BI, SQL Server, and Excel Power Pivot. You’ll then discover how to build and extend your data models to gain additional insights, before covering progressive DAX syntax and functions to understand complex relationships in DAX. Later, you’ll focus on important DAX functions, specifically those related to tables, date and time, filtering, and statistics. Finally, you’ll delve into advanced topics such as how the formula and storage engines work to optimize queries. By the end of this book, you’ll have gained hands-on experience in employing DAX to enhance your data models by extracting new information and gaining deeper insights.
Table of Contents (18 chapters)
1
Section 1: Introduction to DAX for the BI Pro
7
Section 2: Understanding DAX Functions and Syntax
14
Section 3: Taking DAX to the Next Level

Working with percentiles

There are six DAX functions in this function group that will help you calculate percentile values for a given set of data. They are PERCENTILE.EXC, PERCENTILE.INC, PERCENTILEX.EXC, PERCENTILEX.INC, MEDIAN, and MEDIANX.

A percentile is a statistical measure that gives the value where a certain percentage of values in a dataset fall below it. For example, the 30th percentile will be the value in a dataset where 30% of the values fall below it, and the remaining 70% are above it.

Before we start to look at these functions, let's use the following expression to create a new table, which will contain the numbers 1 through to 20:

Numbers = GENERATESERIES ( 1, 20 )

Now, we can use this table as our dataset to help us understand how these functions work. We'll start by looking at the first of these functions.

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