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

【One-Line Pitch】 A practical guide for intermediate Excel users who want to turn spreadsheets into repeatable, scalable analytics workflows using Power Query, Power Pivot, dynamic arrays, AI, and Python. If you already know your way around formulas and PivotTables but still clean data by hand, this book shows you a better path. 【Book Arc】 - **Opening (~0%–10%)**: Sets up the case for modern Excel analytics, explains why classic spreadsheet habits limit reproducibility, and introduces tidy data as the foundation for everything that follows. - **Early (~10%–32%)**: Builds core Power Query skills — importing data, profiling columns, handling nulls, removing duplicates, splitting and filling columns, changing data types, and combining tables through append and merge operations. - **Middle (~32%–48%)**: Moves into relational modeling with Power Pivot: fact and dimension tables, relationships, cardinality, filter direction, hierarchies, and the Data Model behind PivotTables. - **Late (~48%–60%)**: Covers DAX measures and KPIs in Power Pivot, showing how to move beyond calculated columns toward dynamic aggregations that recalculate across categories and time periods. - **Ending (~60%–100%)**: Explores the broader analytics toolkit — dynamic array functions, predictive analytics and AI features in Excel, and Python integration for automated analysis and reporting. The excerpts do not cover the detailed content of these later chapters beyond their stated purpose. 【Key Takeaways】 - **Tidy data is the prerequisite for reliable analysis** (Opening): The book anchors on Hadley Wickham's rule that each variable should form a column. Data stored with multiple columns measuring the same thing is a red flag — restructuring it makes analysis dramatically simpler. - **Power Query brings reproducibility to Excel without requiring code** (Early): Unlike VBA, which has a steep learning curve and limited updates from Microsoft, Power Query records every transformation as an Applied Step. This makes cleaning workflows auditable, reversible, and accessible to non-programmers. - **Power Query solves Excel's structural limitations** (Early): Excel lacks a true null value and caps at roughly 1,048,576 rows. Power Query introduces a dedicated null representation and handles larger data volumes, enabling precise profiling, removal, and replacement of missing values. - **Row-level cleaning and column-level transformation are distinct skill sets** (Early): The book separates these concerns — early chapters focus on sorting, filtering, deduplicating, and filling rows, while later chapters address column transformations like splitting by delimiter, renaming, and data type assignment. - **Append and merge replace VLOOKUP for combining data** (Early): Power Query's append stacks tables vertically, while merge joins them horizontally. Left outer joins keep all records from one table without erroring on unmatched rows, and inner joins keep only matches — a more robust alternative to lookup functions. - **Power Pivot handles what Power Query should not** (Middle): For large datasets and sophisticated relational analysis, Power Pivot's Data Model offers intertable relationships, cardinality awareness, and filter direction that Power Query alone cannot provide. The two tools are complementary, not competing. - **Relationships and filter direction determine model behavior** (Middle): In a one-to-many relationship, filters flow from the "one" side to the "many" side. Understanding cardinality — one-to-one, one-to-many, many-to-many — is essential for troubleshooting and for transitioning models to Power BI. - **DAX measures unlock dynamic aggregation** (Late): Calculated columns fall short when you need results to recalculate across categories and time periods. Explicit DAX measures are the mechanism for building KPIs that respond to slicers and PivotTable context. 【Reading Tips】 - **Deep-read the Power Query chapters if you clean data regularly.** The step-by-step transformations — removing blanks, splitting columns, filling down, merging queries — are the highest-leverage skills for day-to-day work. - **Skim the Power Pivot modeling chapters if you already understand star schemas.** Focus instead on the filter direction and cardinality sections, which are where most self-taught Excel users get stuck. - **Treat Part III as a survey, not a mastery track.** Dynamic arrays, AI, and Python integration are introduced as gateways; the book itself notes that advanced statistics and programming may feel challenging at first. - **Use the companion repository exercises.** The book repeatedly references solution files and exercise workbooks — working through them is the intended way to internalize the Applied Steps mindset. - **Pay attention to the "why" behind tool choices.** The book explicitly addresses when to use Power Query versus Power Pivot versus lookup functions, which is more valuable than memorizing any single technique. 【Coverage Limits】 This guide is based on stratified excerpts covering roughly the first half of the book, with the later chapters on dynamic arrays, AI, and Python represented only by their introductory descriptions. Specific techniques, examples, and chapter-level detail from the second half are not covered here.