
Modern Data Analytics in Excel
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Content
- 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
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