{"product_id":"data-analytics-for-business-practical-guide-to-applied-ai","title":"Data Analytics for Business - Practical Guide to Applied AI","description":"\u003cp\u003eOur review of Data Analytics for Business finds it most useful for practitioners and managers who need a practical bridge between technical methods and real commercial problems. The author draws on more than 20 years of experience delivering big data and artificial intelligence solutions across finance, pharmaceuticals, consumer goods, media, and retail, and the book's essay style makes complex topics approachable without getting lost in equations. For readers seeking actionable guidance on applying \u003cstrong\u003eadvanced analytics\u003c\/strong\u003e to sales and marketing, this book delivers clear lessons and case-based insight.\u003c\/p\u003e\u003ch2\u003eKey Features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndustry experience:\u003c\/strong\u003e The author uses over two decades of delivery experience to illustrate how analytics projects work in real business environments.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eApplied focus:\u003c\/strong\u003e Guidance centers on using \u003cstrong\u003edata mining\u003c\/strong\u003e and machine learning techniques to solve sales and marketing problems rather than theoretical exposition.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCross-industry cases:\u003c\/strong\u003e Summarized cases span financial services, pharmaceuticals, consumer packaged goods, media, and retail, showing adaptability of methods.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEssay structure:\u003c\/strong\u003e Short, focused essays each present a single critical lesson, making concepts easy to digest and reference.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePractical guidelines:\u003c\/strong\u003e The text offers stepwise advice useful to data scientists, data engineers, and business analysts working on real deployments.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eWho It's For\u003c\/h2\u003e\u003cp\u003eThe book is best suited for mid-level data professionals, analytics managers, and business leaders who need to translate analytic capability into measurable sales and marketing outcomes. It helps readers who already have some familiarity with statistics or machine learning and want to see how those tools apply to commercial use cases.\u003c\/p\u003e\u003cp\u003eIt is less appropriate as a first textbook for complete beginners seeking formal instruction in algorithms or as a programmer's API reference; those audiences should look for introductory statistics texts or technical manuals with code examples.\u003c\/p\u003e\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eProvides practical, industry-tested lessons that accelerate understanding of real-world analytics projects.\u003c\/li\u003e\n\u003cli\u003eConcise essays make it easy to find a focused insight without wading through long chapters.\u003c\/li\u003e\n\u003cli\u003eCross-industry examples demonstrate how to adapt methods to different business contexts.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003eNot a step-by-step coding guide, so readers expecting extensive source code or algorithm derivations may find it light on technical detail.\u003c\/li\u003e\u003c\/ul\u003e\u003ch2\u003eSpecifications\u003c\/h2\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eData Analytics for Business\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAuthor \/ Brand\u003c\/td\u003e\n\u003ctd\u003eIra J. Haimowitz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePrimary focus\u003c\/td\u003e\n\u003ctd\u003eApplying analytics, machine learning, and AI to sales and marketing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eApproach\u003c\/td\u003e\n\u003ctd\u003eSeries of essays summarizing critical lessons and cases\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIndustry coverage\u003c\/td\u003e\n\u003ctd\u003eFinancial services, pharmaceuticals, consumer packaged goods, media, retail\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTarget readers\u003c\/td\u003e\n\u003ctd\u003eData scientists, data engineers, business analysts, analytics managers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003ch2\u003eOur Verdict\u003c\/h2\u003e\u003cp\u003eData Analytics for Business is a practical, well-grounded resource for professionals who want actionable guidance on using \u003cstrong\u003emachine learning\u003c\/strong\u003e and analytics in sales and marketing. Its essay format and industry-spanning examples make it good value for practitioners and managers seeking to move projects from concept to impact, though those needing detailed code or algorithmic derivations should supplement it with technical manuals.\u003c\/p\u003e\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003eDoes this book include technical code or algorithms?\u003c\/strong\u003e\u003cbr\u003eThe book focuses on lessons and cases rather than step-by-step code, so it provides conceptual and practical guidance instead of extensive programming examples.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhich industries are covered by the case studies?\u003c\/strong\u003e\u003cbr\u003eSummarized cases and examples come from financial services, pharmaceuticals, consumer packaged goods, media, and retail.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho will benefit most from this book?\u003c\/strong\u003e\u003cbr\u003eMid-level data professionals, analytics managers, and business leaders seeking to apply analytics to sales and marketing will gain the most value.\u003c\/p\u003e","brand":"Ira J. 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