This collaborative effort from five experienced data professionals — Jun Shan, Haibin Li, Matt Goldwasser, Upom Malik, and Benjamin Johnston — moves past basic SQL syntax into the advanced techniques that real data analytics work demands. SQL for Data Analytics focuses on uncovering genuine insights, not just writing queries.
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In StockA lot of SQL books stop at teaching syntax; SQL for Data Analytics is more interested in what comes after — how to actually use SQL to answer real business questions and uncover genuine insight from data. With five contributing authors bringing varied data and analytics backgrounds, the book covers advanced querying techniques, window functions, data transformation strategies, and the kind of analytical thinking that separates someone who knows SQL syntax from someone who can actually use it to drive decisions. The real-world framing runs throughout, with examples and exercises built around the kinds of messy, practical data problems analysts actually encounter rather than clean, simplified textbook scenarios. At 336 pages, the book stays focused and applied, making it a solid choice for readers who already have basic SQL familiarity and want to level up into genuinely advanced, analytics-focused technique. For data analysts and aspiring analysts who want SQL skills that translate directly into real-world analytical work, this delivers exactly that kind of applied depth.
Author:
Jun Shan, Haibin Li, Matt Goldwasser, Upom Malik, Benjamin Johnston
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