For Marketing, Sales, and Customer Relationship Management
by Gordon S. Linoff
Recommended by Kirk Borne
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Data Mining Techniques delivers a broad, method-oriented introduction to common algorithms with business-flavored examples and pragmatic advice on when each approach fits a problem. Strengths are clear explanations of methods and guidance for scoping projects; limitations are a textbook voice, recurring algebraic derivations, and relatively few runnable, code-first examples. Readers seeking immediate notebook-style recipes or cutting-edge neural workflows may feel frustrated. Best read in chunks while pairing chapters with hands-on experiments in your toolset.
This summary and audience guidance are AI-generated from the book's description and its recommendation history, last updated June 2026. Recommendations, quotes and sources elsewhere on this page come from published material and are not generated.
The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new editionmore than 50% new and r...
Difficulty:hard
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View available editions on AmazonWhy recommended
Recommended by 1 source.
People and public figures who have recommended this book.
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
Kirk Borne
“If you are just starting your #MachineLearning learning journey, I recommend this as a great beginner’s book: “#DataMining Techniques for Marketing, Sales and Customer Relationship Management” (Third Edition) #BigData #DataScience #AI #DataScientist #CX”
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Consider Data Science for Business by Foster Provost. Recommended by 1 sources.
“Data Science for Business reads like a careful, concept-first introduction to using data in managerial decisions. It lays out the probabilistic thinking behind common algorithms and ties analytic choices to business questions, with worked examples and case fragments. The most useful part is the emphasis on when an analytic approach produces business value versus when it only moves metrics. Limitation: it rarely serves as a code-first how-to, and some chapters linger on formal descriptions that slow the pace.”
Each recommendation is collected from a public source — interviews, articles, or curated lists — and linked to its original URL. Books with many verifiable recommendations from respected people rank higher.
