Data Mining Techniques
For Marketing, Sales, and Customer Relationship Management
by Gordon S. Linoff
Reading Profile
Should I read this?
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.
Read this if...
- •a mid-level data analyst at a mid-size company transitioning from reporting to predictive models, who needs conceptual grounding to scope projects and brief engineers about algorithm choices.
- •a master's student in statistics or applied data science preparing for coursework, who wants a method-oriented primer that balances intuition with some mathematical detail.
- •a product manager inside a legacy enterprise evaluating data-mining initiatives, who needs enough technical fluency to judge vendor proposals and prioritize business use cases.
Skip this if...
- •you'll likely put it down when chapters shift into algebra-heavy derivations and long procedural walkthroughs instead of runnable, copy-paste examples.
- •annoying if you prefer a code-first, notebook-style approach with reproducible examples — the book lacks hands-on exercises or runnable notebooks.
- •not a good fit if you want the very latest deep-learning toolchains or bite-sized pop summaries; the pace can feel dry and detail-heavy for readers seeking quick inspiration.
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...
Before You Buy
Reading Specifications
Difficulty:hard
Audience Fit
- a mid-level data analyst at a mid-size company transitioning from reporting to predictive models, who needs conceptual grounding to scope projects and brief engineers about algorithm choices.
- a master's student in statistics or applied data science preparing for coursework, who wants a method-oriented primer that balances intuition with some mathematical detail.
- a product manager inside a legacy enterprise evaluating data-mining initiatives, who needs enough technical fluency to judge vendor proposals and prioritize business use cases.
- you'll likely put it down when chapters shift into algebra-heavy derivations and long procedural walkthroughs instead of runnable, copy-paste examples.
- annoying if you prefer a code-first, notebook-style approach with reproducible examples — the book lacks hands-on exercises or runnable notebooks.
- not a good fit if you want the very latest deep-learning toolchains or bite-sized pop summaries; the pace can feel dry and detail-heavy for readers seeking quick inspiration.
Check formats, pricing, and availability options for Kindle, physical print, or audiobooks directly.
View available editions on AmazonKey themes
Why recommended
Recommended by 1 source and appears in Data Mining.
Recommended by notable people
People and public figures who have recommended this book.
Recommendation Signals
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”
Appears In
Not sure if this is the right fit?
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.”
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Charu C. AggarwalHow recommendation signals are reviewed
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.