Preface

Data Modeling with Microsoft Excel is an essential resource for anyone looking to harness the full potential of Microsoft Excel in data analysis.

This book delves deep into the advanced features of Excel, specifically focusing on Power Pivot, Data Analysis Expressions (DAX), and Cube Functions. These tools are pivotal for anyone working with large datasets and seeking to perform complex data modeling and analysis efficiently.

This book is not just about learning the functionalities; it’s about understanding how to apply these tools in real-world scenarios to derive meaningful insights from your data. Whether you are a business analyst, a data scientist, or someone who regularly works with data in Excel, this book will equip you with the skills to transform the way you interpret and present data, enhancing your analytical capabilities and decision-making processes.

Who this book is for

This book is for Excel users looking for hands-on and effective methods to manage and analyze large volumes of data within Microsoft Excel using Power Pivot. If you are new or already conversant with Excel’s Data Analytics tools, this book will give you insight into practical ways to apply Excel’s Power Pivot, Power Query, DAX and Cube functions to save you time on routine data management tasks.

What this book covers

Chapter 1, Getting Started with Data Modeling, introduces the concept of data modeling, explaining its significance in today’s data-driven world. Readers will learn the basics of data modeling in Excel and understand why it is a crucial skill for effective data analysis.

Chapter 2, Data Structuring for Data Models, focuses on organizing data to optimize its use in Power Pivot. The chapter provides strategies and best practices for structuring data tables to facilitate efficient data analysis and modeling.

Chapter 3, Preparing Your Data for the Data Model, guides readers through the process of cleaning and transforming raw data into a more usable format using Power Query, setting the stage for effective data modeling.

Chapter 4, Data Modeling with Power Pivot, will show you how to use Power Pivot for advanced data modeling, including techniques for connecting tables using relationships.

Chapter 5, Creating DAX Calculations from Your Data Model, introduces DAX (Data Analysis Expressions), teaching you how to create powerful calculations and measures directly within your data models.

Chapter 6, Creating Cube Functions from Your Data Model, explores Cube Functions as an alternative to DAX, offering flexibility in extracting and manipulating data from the data model.

Chapter 7, Communicating Insights from Your Data Model Using Dashboards, emphasizes the importance of dashboards in communicating data insights. It covers how to create effective dashboards that convey the story behind the data clearly and compellingly.

Chapter 8, Visualization Elements for Your Dashboard, will show you various visualization tools such as slicers, pivot charts, conditional formatting, and shapes, and how to use them to enhance the effectiveness of your dashboards.

Chapter 9, Choosing the Right Design Themes, delves into the principles of design in the context of data presentation, focusing on the strategic use of colors and themes to create visually appealing and informative dashboards.

Chapter 10, Publication and Deployment, guides you through the process of publishing and deploying your Excel models and dashboards, ensuring that the insights are accessible and understandable to end-users, online and offline.

To get the most out of this book

An understanding of Excel’s features like Tables, Pivot Tables and some basic aggregating functions will be useful but not a requirement to come along.

Software/hardware covered in the book	Operating system requirements
Excel	Windows, macOS, or Linux
Power BI

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “We can connect these two normalized tables using the common column Course ID.”

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “we can have separate lookup tables that will give us the details for each Customer ID, City, and Product ID.”

Tips or important notes

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