Modern Data Analytics in Excel: Using Power Query, Power Pivot, and More for Enhanced Data Analytics

✍ Scribed by George Mount

• Publisher: O'Reilly Media
• Year: 2024
• Tongue: English
• Leaves: 244
• Edition: 1
• Category: Library

✦ Synopsis

If you haven't modernized your data cleaning and reporting processes in Microsoft Excel, you're missing out on big productivity gains. And if you're looking to conduct rigorous data analysis, more can be done in Excel than you think. This practical book serves as an introduction to the modern Excel suite of features along with other powerful tools for analytics.

George Mount of Stringfest Analytics shows business analysts, data analysts, and business intelligence specialists how to make bigger gains right from your spreadsheets by using Excel's latest features. You'll learn how to build repeatable data cleaning workflows with Power Query, and design relational data models straight from your workbook with Power Pivot. You'll also explore other exciting new features for analytics, such as dynamic array functions, AI-powered insights, and Python integration.

Learn how to build reports and analyses that were previously difficult or impossible to do in Excel. This book shows you how to:
• Build repeatable data cleaning processes for Excel with Power Query
• Create relational data models and analysis measures with Power Pivot
• Pull data quickly with dynamic arrays
• Use AI to uncover patterns and trends from inside Excel
• Integrate Python functionality with Excel for automated analysis and reporting

✦ Table of Contents

Cover
Copyright
Table of Contents
Preface
Learning Objective
Prerequisites
Technical Requirements
Technological Requirements
How I Got Here
What Is “Modern Analytics”? Why Excel?
Book Overview
Part I, Data Cleaning and Transformation with Power Query
Part II, Data Modeling and Analysis with Power Pivot
Part III, The Excel Data Analytics Toolkit
End-of-Chapter Exercises
This Is Not a Laundry List
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments
Part I. Data Cleaning and Transformation with Power Query
Chapter 1. Tables: The Portal to Modern Excel
Creating and Referring to Table Headers
Viewing the Table Footers
Naming Excel Tables
Formatting Excel Tables
Updating Table Ranges
Organizing Data for Analytics
Conclusion
Exercises
Chapter 2. First Steps in Excel Power Query
What Is Power Query?
Power Query as Excel Myth Buster
“Excel Is Not Reproducible”
“Excel Does Not Have a True null”
“Excel Can’t Process More Than 1,048,576 Rows”
Power Query as Excel’s ETL Tool
Extract
Transform
Load
A Tour of the Power Query Editor
The Ribbon Menu
Queries
The Imported Data
Exiting the Power Query Editor
Returning to the Power Query Editor
Data Profiling in Power Query
What Is Data Profiling?
Exploring the Data Preview Options
Overriding the Thousand-Row Limit
Closing Out of Data Profiling
Conclusion
Exercises
Chapter 3. Transforming Rows in Power Query
Removing the Missing Values
Refreshing the Query
Splitting Data into Rows
Filling in Headers and Cell Values
Replacing Column Headers
Filling Down Blank Rows
Conclusion
Exercises
Chapter 4. Transforming Columns in Power Query
Changing Column Case
Delimiting by Column
Changing Data Types
Deleting Columns
Working with Dates
Creating Custom Columns
Loading & Inspecting the Data
Calculated Columns Versus Measures
Reshaping Data
Conclusion
Exercises
Chapter 5. Merging and Appending Data in Power Query
Appending Multiple Sources
Connecting to External Excel Workbooks
Appending the Queries
Understanding Relational Joins
Left Outer Join: Think VLOOKUP()
Inner Join: Only the Matches
Managing Your Queries
Grouping Your Queries
Viewing Query Dependencies
Conclusion
Exercises
Part II. Data Modeling and Analysis with Power Pivot
Chapter 6. First Steps in Power Pivot
What Is Power Pivot?
Why Power Pivot?
Power Pivot and the Data Model
Loading the Power Pivot Add-in
A Brief Tour of the Power Pivot Add-In
Data Model
Calculations
Tables
Relationships
Settings
Conclusion
Exercises
Chapter 7. Creating Relational Models in Power Pivot
Connecting Data to Power Pivot
Creating Relationships
Identifying Fact and Dimension Tables
Arranging the Diagram View
Editing the Relationships
Loading the Results to Excel
Understanding Cardinality
One-to-One Cardinality
One-to-Many Relationships
Many-to-Many Relationships
Why Does Cardinality Matter?
Understanding Filter Direction
Filtering orders with users
Filtering users with orders
Filter Direction and Cardinality
From Design to Practice in Power Pivot
Creating Columns in Power Pivot
Calculating in Power Query Versus Power Pivot
Example: Calculating Profit Margin
Recoding Column Values with SWITCH()
Creating and Managing Hierarchies
Creating a Hierarchy in Power Pivot
Using Hierarchies in the PivotTable
Loading the Data Model to Power BI
Power BI as the Third Piece of “Modern Excel”
Importing the Data Model to Power BI
Viewing the Data in Power BI
Conclusion
Exercises
Chapter 8. Creating Measures and KPIs in Power Pivot
Creating DAX Measures
Creating Implicit Measures
Creating Explicit Measures
Creating KPIs
Adjusting Icon Styles
Adding the KPI to the PivotTable
Conclusion
Exercises
Chapter 9. Intermediate DAX for Power Pivot
CALCULATE() and the Importance of Filter Context
CALCULATE() with One Criterion
CALCULATE() with Multiple Criteria
AND Conditions
OR Conditions
CALCULATE() with ALL()
Time Intelligence Functions
Adding a Calendar Table
Creating Basic Time Intelligence Measures
Conclusion
Exercises
Part III. The Excel Data Analytics Toolkit
Chapter 10. Introducing Dynamic Array Functions
Dynamic Array Functions Explained
What Is an Array in Excel?
Array References
Array Formulas
An Overview of Dynamic Array Functions
Finding Distinct and Unique Values with UNIQUE()
Finding Unique Versus Distinct Values
Using the Spill Operator
Filtering Records with FILTER()
Adding a Header Column
Filtering by Multiple Criteria
Sorting Records with SORTBY()
Sorting by Multiple Criteria
Sorting by Another Column Without Printing It
Creating Modern Lookups with XLOOKUP()
XLOOKUP() Versus VLOOKUP()
A Basic XLOOKUP()
XLOOKUP() and Error Handling
XLOOKUP() and Looking Up to the Left
Other Dynamic Array Functions
Dynamic Arrays and Modern Excel
Conclusion
Exercises
Chapter 11. Augmented Analytics and the Future of Excel
The Growing Complexity of Data and Analytics
Excel and the Legacy of Self-Service BI
Excel for Augmented Analytics
Using Analyze Data for AI Powered Insights
Building Statistical Models with XLMiner
Reading Data from an Image
Sentiment Analysis with Azure Machine Learning
Conclusion
Exercises
Chapter 12. Python with Excel
Reader Prerequisites
The Role of Python in Modern Excel
A Growing Stack Requires Glue
Network Effects Mean Faster Development Time
Bring Modern Development to Excel
Using Python and Excel Together with pandas and openpyxl
Other Python Packages for Excel
Demonstration of Excel Automation with pandas and openpyxl
Cleaning Up the Data in pandas
Summarizing Findings with openpyxl
Adding a Styled Data Source
Conclusion
Exercises
Chapter 13. Conclusion and Next Steps
Exploring Excel’s Other Features
LET() and LAMBDA()
Power Automate, Office Scripts, and Excel Online
Continued Exploration of Power Query and Power Pivot
Power Query and M
Power Pivot and DAX
Power BI for Dashboards and Reports
Azure and Cloud Computing
Python Programming
Large Language Models and Prompt Engineering
Parting Words
Index
About the Author
Colophon

✦ Subjects

Data Analysis; Analytics; Python; Relational Databases; Excel; Data Modeling; Power Pivot; Power Query; DAX; openpyxl