100 Best Data Mining Books of All Time
We've researched and ranked the best data mining books in the world, based on recommendations from world experts, sales data, and millions of reader ratings. Learn more
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll... more
Kirk BorneGreat book for Business Analytics and for building #AnalyticThinking >> “#DataScience for Business — What You Need to Know about #DataMining and Data-Analytic Thinking”: https://t.co/e9rAFnVYYQ #BigData #MachineLearning #DataStrategy #AnalyticsStrategy #Algorithms https://t.co/yEblfU2MZd (Source)
Concise and to the point — the book can be read during a week. During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.
Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.
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Kirk BorneRecent top-selling books in #AI & #MachineLearning: https://t.co/Ij9I7SzR4d ————— #BigData #DataScience #DataMining #Algorithms #PredictiveAnalytics #Python ————— ...in the TOP 10: 1)The Hundred-Page ML Book: https://t.co/dQ7nP6gwP0 2)Hands-on ML with...: https://t.co/Y0Iz3GbtGP https://t.co/72rAFN1FwW (Source)
Roger D. PengThis book is written by a powerhouse of authors in the machine learning community, true authorities in the field. But beyond that, they’re also great writers. (Source)
New York Times Bestseller
"Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade."
-New York Times Book Review
"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."
-Rachel Maddow, author of Drift
"A serious... more
Bill GatesAnyone interested in politics may be attracted to Nate Silver’s The Signal and the Noise: Why So Many Predictions Fail—but Some Don't. Silver is the New York Times columnist who got a lot of attention last fall for predicting—accurately, as it turned out–the results of the U.S. presidential election. This book actually came out before the election, though, and it’s about predictions in many... (Source)
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into... more
--Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline's techniques. Packed with... more
Kirk Borne🌟📘📊📈Awesome new book >> #DeepLearning Illustrated — A Visual, Interactive Guide to Artificial Intelligence” https://t.co/xIW48MskrR by @JonKrohnLearns ——————— #BigData #Analytics #DataScience #AI #MachineLearning #Algorithms #NeuralNetworks https://t.co/JKSrVRLpS0 (Source)
Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to... more
Kirk BorneFind more than 40 useful #PredictiveModeling articles here at @DataScienceCtrl https://t.co/KdcvLRffRk #abdsc ———— #BigData #DataScience #AI #MachineLearning #Forecasting #Statistics #PredictiveAnalytics ——— +This is the best book on the subject: https://t.co/SmsepmniHi https://t.co/amBJHCJSHN (Source)
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets,... more
Do you want to understand how to manage databases without all the confusion?
Well than, this is your go to guide to help you master SQL programming in no time!
This book breaks down the fundamentals elements that are essential to make you proficient in SQL programming and database management
By the end of this book you will be confident enough to take on any problems that encompass SQL
SQL software can be complex,... more
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run... more
Written by Wes McKinney, the main author of the pandas library, Python for Data Analysis also serves as a practical, modern introduction to scientific computing in Python for data-intensive... more
Programming Collective Intelligence takes you into the world of machine learning... more
Until now. Beyond reading email and surfing the Web, we will soon be checking our vital signs on our phone. We can already continuously monitor our heart rhythm, blood glucose... more
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
lessEric Weinstein[Eric Weinstein recommended this book on Twitter.] (Source)
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one... more
Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the... more
R in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.
About the Technology
R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It... more
Big Data shows how to build these systems using an architecture that takes advantage of... more
Vicki BoykisThis book remains a great read if you want to understand how modern data architecture works, and especially distributed data systems. (Source)
This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.
Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked... more
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
In particular, the book stresses the following basic principles as fundamental to becoming a good data scientist: "Valuing Doing the Simple Things Right," laying the groundwork of what really matters in analyzing data; "Developing Mathematical Intuition,"... more
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the...
moreNassim Nicholas TalebVery comprehensive, sufficiently technical to get most of the plumbing behind machine learning. Very useful as a reference book (actually, there is no other complete reference book). The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique). Uses some mathematical statistics without the burdens of measure theory and avoids the obvious but complicated... (Source)
Kirk BorneIf 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) https://t.co/gSLkCSwLDF #BigData #DataScience #AI #DataScientist #CX https://t.co/2SyjLCqObv (Source)
You'll first cover the fundamentals of databases and the SQL language, then build skills by analyzing data from the U.S. Census and other federal and state government agencies. With exercises and real-world examples in each... more
Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.
Mark begins by... more
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
You'll explore the basic operations and common functions of Spark's structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of... more
Kirk BorneThis awesome book’s 2nd edition is now available! >> “Introduction to #DataMining” https://t.co/ZTna3ZQIGv #BigData #DataScience #MachineLearning https://t.co/9NuK5RUxv8 (Source)
The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and... more
Solve real business problems with Excel--and build your competitive advantage Quickly transition from Excel basics to sophisticated analytics... more
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts... more
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak?
The key to answering these questions, and many more, is big data. “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw... more
"The Freakonomics of big data." --Stein Kretsinger, founding executive of Advertising.com
Award-winning - Used by over 30 universities - Translated into 9 languages
An introduction for everyone. In this rich, fascinating -- surprisingly accessible -- introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a "how to" for hands-on... more
From the stock market to genomics laboratories, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models--from linear regression to random walks and far beyond--that can turn anyone into a genius. At the core of the book is Page's... more
Along the way, you'll experiment... more
This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models.
The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component... more
Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to...
more"This unique and essential guide to human visual perception and related cognitive principles will enrich courses on information visualization and empower designers to see their way forward. Ware's updated review of empirical research and interface design examples will do much to accelerate innovation and adoption of information visualization."
—Ben Shneiderman, University of Maryland
"Colin Ware is the perfect person to write this book, with a long history of prominent contributions to the visual interaction with machines and to information visualization directly. It goes a...
moreDon't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its... more
This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation
Author Fabio... more
In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re... more
These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as... more
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you've learned, and concludes with exercises, most of which involve writing R code. more
Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the... more
This book offers advice on how to interpret and incorporate data into an organization’s overall marketing strategy. It is designed to help marketers improve customer relationships, enhance the targeting of their marketing efforts, align marketing activities with ultimate goals and objectives, and gain insight into the... more
"Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature."--computingreviews.com
Incorporating a new focus on data visualization and time series forecasting, "Data Mining for Business Intelligence," Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers... more
* Real datasets are used extensively.
* All data analysis is supported by R coding.
* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.
* Leads the student to think... more
Learn how to use a problem's "weight" against itself to:
Break down seemingly complex data problems into simplified parts
Use alternative data analysis techniques to examine them
Use human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough problems
Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at... more
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you:
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When Brittany Kaiser joined Cambridge Analytica--the UK-based political consulting firm funded by conservative billionaire and Donald Trump patron Robert... more
Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results--even if you don't have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you... more
Authors Denise Koessler Gosnell and Matthias Broecheler show you how companies today are successfully applying graph thinking in distributed production environments. You'll also learn the Graph Schema Language, a set of terminology and visual... more
Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Machine learning systems help you find valuable insights and patterns in data,... more
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
Every day we produce loads of data about ourselves simply by living in the modern world: we click web pages, flip channels, drive through automatic toll booths, shop with credit cards, and make cell phone calls. Now, in one of the greatest undertakings of the twenty-first century, a savvy group of mathematicians and computer scientists is beginning to sift through this data to dissect us and map out our next steps. Their goal? To manipulate our behavior -- what we... more
Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both... more
Alex Gorelik, CTO and founder of Waterline... more
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.
All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are... more
Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs... more
Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the... more
Don't have time to read the top Data Mining books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.