Modern Data Analytics in Excel Using Power Query, Power Pivot, and More for Enhanced Data Analytics (George Mount)（Z-Library）

# Modern Data Analytics in Excel ## 【One-Line Pitch】 A practical, hands-on guide for intermediate Excel users who want to modernize their data workflows using Power Query, Power Pivot, DAX, dynamic arrays, and even Python—without leaving the familiar Excel environment. If you've been told "Excel can't do that," this book shows you how it now can. ## 【Book Arc】 - **Opening (~0%–9%)**: Sets the stage for why modern Excel matters, outlines prerequisites (intermediate Excel skills like VLOOKUP, PivotTables, conditional logic), and explains the book's three-part structure: Power Query for cleaning, Power Pivot for modeling, and the broader analytics toolkit. - **Early (~16%–28%)**: Orients readers to the broader analytics ecosystem—noting that Power Query and Power Pivot also appear in Power BI—and establishes the book's philosophy: this is a curated set of high-impact tools, not an exhaustive laundry list. Includes practical guidance on using the companion repository and end-of-chapter exercises. - **Early (~28%–34%)**: Dives into Chapter 1 on Excel tables as "the portal to modern Excel," covering structured references, named ranges, table footers with Total Row, formatting options, and how tables keep formulas accurate as data changes. - **Middle (~38%–47%)**: Continues the table fundamentals with practical applications like dynamic header capitalization and calculated columns, then transitions into Chapter 2's introduction to Power Query—positioning it as a "myth buster" that solves Excel's traditional reproducibility problems. - **Middle (~53%)**: Begins debunking common Excel criticisms, starting with reproducibility (Power Query makes workflows repeatable) and the "1,048,576 row limit" myth (Power Query can handle larger datasets). Excerpts do not cover the full resolution of these arguments or later chapters on Power Pivot, DAX, dynamic arrays, AI, and Python integration. ## 【Key Takeaways】 - **Excel tables are the foundation for modern analytics** (Early): Structured references like `=Table1[#Headers]` and `=Table1[#Totals]` replace fragile cell coordinates, making formulas automatically adapt when data is added or removed. This is the prerequisite for everything else in the book. - **Named tables beat cell references for reliability** (Early): Renaming `Table1` to something descriptive like `sales` (via Name Manager) improves readability and prevents formula breakage when tables change size or position. - **Table footers give you instant aggregation** (Early): The Total Row feature lets you quickly compute sums, averages, or maximums per column—like finding the max sales in the `fresh` category—without writing separate formulas. - **Power Query solves Excel's reproducibility problem** (Middle): Traditional workbooks fail the reproducibility test when hard-coded values or deleted columns make results impossible to recreate. Power Query records your transformation steps, so the same inputs always produce the same outputs. - **Power Query is a low-code ETL tool** (Middle): It connects to, combines, and refines data from many sources through a user-friendly interface—merging tables, transforming formats, and aggregating without requiring programming skills. - **The "Excel can't handle big data" myth is outdated** (Middle): While worksheets cap at ~1,048,576 rows, Power Query extends Excel's reach into larger datasets, challenging the assumption that you must switch tools for bigger data. - **Skills transfer beyond Excel** (Early): Power Query and Power Pivot are also core to Power BI, so mastering them in Excel builds portable skills for Microsoft's broader analytics ecosystem. ## 【Reading Tips】 - **Skim the front matter** (~0%–16%): The prerequisites list and book overview are worth a quick read, but the acknowledgments and publisher boilerplate can be skipped. - **Deep-read Chapter 1 on tables** (~28%–44%): This is foundational—master structured references, named ranges, and Total Row before moving on. The penguins exercise set is worth completing. - **Pay attention to the "myth buster" framing** (~47%–53%): The author uses common Excel criticisms to teach Power Query's value proposition. Understanding *why* these myths existed helps you appreciate what Power Query actually solves. - **Don't skip the exercises**: The author explicitly warns against this—the companion repository has chapter-by-chapter practice files and solutions, which are essential for retention. - **Note the version requirements** (~6%): The book assumes Windows with Microsoft 365 Excel; Mac compatibility varies. Check your setup before diving in. ## 【Coverage Limits】 This guide covers the book's front matter, philosophy, and the opening chapters on Excel tables and Power Query fundamentals. The excerpts do not cover the detailed Power Query transformation techniques, Power Pivot/DAX modeling, dynamic arrays, AI features, or Python integration covered in later chapters. ##