Best Data Mining Books

Data Mining is an art that takes tremendous amounts of knowledge, skill, and hard work to master.

1. SQL QuickStart Guide: The Simplified Beginner's Guide to Managing, Analyzing, and Manipulating Data With SQL

Author: by Walter Shields
ClydeBank Media LLC
English
251 pages

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“THE BEST SQL BOOK FOR BEGINNERS IN 2021 – HANDS DOWN!”*INCLUDES FREE ACCESS TO A SAMPLE DATABASE, SQL BROWSER APP, COMPREHENSION QUIZES & SEVERAL OTHER DIGITAL RESOURCES! Not sure how to prepare for the data-driven future? This book shows you EXACTLY what you need to know to successfully use the SQL programming language to enhance your career!

#1 NEW RELEASE & #1 BEST SELLER *Are you a developer who wants to expand your mastery to database management? Then you NEED this book. Buy now and start reading today! Are you a project manager who needs to better understand your development team’s needs?

A decision maker who needs to make deeper data-driven analysis? Everything you need to know is included in these pages! The ubiquity of big data means that now more than ever there is a burning need to warehouse, access, and understand the contents of massive databases quickly and efficiently.

That’s where SQL comes in. SQL is the workhorse programming language that forms the backbone of modern data management and interpretation. Any database management professional will tell you that despite trendy data management languages that come and go, SQL remains the most widely used and most reliable to date, with no signs of stopping.


2. Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

Author: by Ron Kohavi
Cambridge University Press
English
288 pages

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Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests.”A/B testing is the gold standard of creating verifiable and repeatable experiments, and this book is its definitive text” – Steve Blank, father of modern entrepreneurship, author of The Startup Owner’s Manual and The Four Steps to the Epiphany”This book is a great resource for executives, leaders, researchers or engineers looking to use online controlled experiments” – Harry Shum, Executive Vice President, Microsoft Artificial Intelligence and Research Group”A great book that is both rigorous and accessible.

Readers will learn how to bring trustworthy controlled experiments, which have revolutionized internet product development, to their organizations” – Adam D’Angelo, Co-founder and CEO of Quora and prior CTO of Facebook “Kohavi, Tang and Xu have a wealth of experience and excellent advice to convey, so the book has lots of practical real world examples and lessons learned over many years of the application of these techniques at scale.” – Jeff Dean, Google Senior Fellow, and SVP, Google Research”The secret sauce for a successful online business is experimentation.


3. The Hundred-Page Machine Learning Book

Author: by Andriy Burkov
English
160 pages
199957950X

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Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics both theory and practice that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.”Aurlien Gron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: “The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!.

Burkov doesn’t hesitate to go into the math equations: that’s one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field.”Karolis Urbonas, Head of Data Science at Amazon: “A great introduction to machine learning from a world-class practitioner.”Chao Han, VP, Head of R&D at Lucidworks: “I wish such a book existed when I was a statistics graduate student trying to learn about machine learning.”Sujeet Varakhedi, Head of Engineering at eBay: “Andriy’s book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.”Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: “A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.”Vincent Pollet, Head of Research at Nuance: “The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.”Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: “This is a compact how to do data science manual and I predict it will become a go-to resource for academics and practitioners alike.


4. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Author: by Peter Bruce
O'Reilly Media
English
368 pages

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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that “learn” from dataUnsupervised learning methods for extracting meaning from unlabeled data.


5. Teach Yourself Data Analytics in 30 Days: Learn to use Python and Jupyter Notebooks by exploring fun, real-world data projects

Author: by David Clinton
English
136 pages
1777721008

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Would you like to learn how to extract useful insights from all the data around you without having to take years’ worth of courses? College data science programs teach many valuable skills, but sometimes all you need is some quick and direct tools.

Welcome to Teach Yourself Data Analytics in 30 Days. The book’s curriculum is organized into eight data “stories.” The stories are interesting on their own, but there’s no doubt what they’re really all about is Python data analytics. Each story/chapter contains all the information you would need to go out and get the raw data and then write the Python analytics code necessary to solve a specific problem.

Once you’ve worked through the whole book, you’ll have enough Python skills to solve a wide range of data problems on your own. If you’re motivated and have some time to invest, there’s no reason you can’t use those stories to teach yourself data analytics in 30 days.

You’ll find everything you need to build your own basic data analytics skills, including:Getting Python up and running on Jupyter Notebooks (or, if you prefer, JupyterLab)Finding and cleaning data sourcesPlotting your dataUsing Python functionsUnderstanding results through domain knowledge and tools like regression lines


6. Data Science from Scratch: First Principles with Python

Author: by Joel Grus
O'Reilly Media
English
406 pages

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To really learn data science, you should not only master the toolsdata science libraries, frameworks, modules, and toolkitsbut also understand the ideas and principles underlying them. Updated for Python 3. 6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist.

Packed with New material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data. Get a crash course in PythonLearn the basics of linear algebra, statistics, and probabilityand how and when they’re used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest neighbors, Nave Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases..


7. Learning SQL: Generate, Manipulate, and Retrieve Data

Author: by Alan Beaulieu
O'Reilly Media
English
384 pages

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As data floods into your company, you need to put it to work right awayand SQL is the best tool for the job. With the latest edition of this introductory guide, author Alan Beaulieu helps developers get up to speed with SQL fundamentals for writing database applications, performing administrative tasks, and generating reports.

You’ll find new chapters on SQL and big data, analytic functions, and working with very large databases. Each chapter presents a self-contained lesson on a key SQL concept or technique using numerous illustrations and annotated examples. Exercises let you practice the skills you learn.

Knowledge of SQL is a must for interacting with data. With Learning SQL, you’ll quickly discover how to put the power and flexibility of this language to work. Move quickly through SQL basics and several advanced featuresUse SQL data statements to generate, manipulate, and retrieve dataCreate database objects, such as tables, indexes, and constraints with SQL schema statementsLearn how datasets interact with queries; understand the importance of subqueriesConvert and manipulate data with SQL’s built-in functions and use conditional logic in data statements


8. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Author: by Trevor Hastie
Springer
English
767 pages

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This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics.

It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting-the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.


9. Python for Excel: A Modern Environment for Automation and Data Analysis

Author: by Felix Zumstein
O'Reilly Media
English
338 pages

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While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it’s the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein-creator of xlwings, a popular open source package for automating Excel with Python-shows experienced Excel users how to integrate these two worlds efficiently.

Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started.

Use Python without extensive programming knowledgeGet started with modern tools, including Jupyter notebooks and Visual Studio codeUse pandas to acquire, clean, and analyze data and replace typical Excel calculationsAutomate tedious tasks like consolidation of Excel workbooks and production of Excel reportsUse xlwings to build interactive Excel tools that use Python as a calculation engineConnect Excel to databases and CSV files and fetch data from the internet using Python codeUse Python as a single tool to replace VBA, Power Query, and Power Pivot

10. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Author: by Foster Provost
O'Reilly Media
English
414 pages

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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect.

This guide also helps you understand the many data-mining techniques in use today. 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 not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

Understand how data science fits in your organizationand how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates.

11. Introduction to Statistics: An Intuitive Guide for Analyzing Data and Unlocking Discoveries

Author: by Jim Frost
English

255 pages
1735431109

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Learn Statistics Without Fear! Build a solid foundation in data analysis. Be confident that you understand what your data are telling you and that you can explain the results to others! I’ll help you intuitively understand statistics by using simple language and deemphasizing formulas.

This guide starts with an overview of statistics and why it is so important. We proceed to essential statistical skills and knowledge about different types of data, relationships, and distributions. Then we move to using inferential statistics to expand human knowledge, how it fits into the scientific method, and how to design and critique experiments.

Learn the fundamentals of statistics. Why is the field of statistics so vital in our data-driven society? Interpret graphs and summary statistics. Find relationships between different types of variables. Understand the properties of data distributions. Use measures of central tendency and variability.

Interpret correlations and percentiles. Use probability distributions to calculate probabilities. Learn about the normal and binomial distributions in depth. Grasp the differences between descriptive and inferential statistics. Use data collection methodologies properly and understand sample size considerations. Design and critique scientific experimentswhether it’s your own or another researcher’s.

12. Data Sketches: A journey of imagination, exploration, and beautiful data visualizations (AK Peters Visualization Series)

Author: by Nadieh Bremer
A K Peters/CRC Press
English
428 pages

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In Data Sketches, Nadieh Bremer and Shirley Wu document the deeply creative process behind 24 unique data visualization projects, and they combine this with powerful technical insights which reveal the mindset behind coding creatively. Exploring 12 different themes from the Olympics to Presidents & Royals and from Movies to Myths & Legends each pair of visualizations explores different technologies and forms, blurring the boundary between visualization as an exploratory tool and an artform in its own right.

This beautiful book provides an intimate, behind-the-scenes account of all 24 projects and shares the authors’ personal notes and drafts every step of the way. The book features: Detailed information on data gathering, sketching, and coding data visualizations for the web, with screenshots of works-in-progress and reproductions from the authors’ notebooks Never-before-published technical write-ups, with beginner-friendly explanations of core data visualization concepts Practical lessons based on the data and design challenges overcome during each project Full-color pages, showcasing all 24 final data visualizations This book is perfect for anyone interested or working in data visualization and information design, and especially those who want to take their work to the next level and are inspired by unique and compelling data-driven storytelling.

13

Database Internals: A Deep Dive into How Distributed Data Systems Work
Author: by Alex Petrov
O'Reilly Media
English
376 pages

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When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.

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 and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.

This book examines:Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for eachStorage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead LogDistributed systems: Learn step-by-step how nodes and processes connect and build complex communication patternsDatabase clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency

14. Bayesian Statistics The Fun Way: Understanding Statistics And Probability With Star Wars, Lego, And Rubber Ducks

Author: by Will Kurt
English
256 pages
1593279566

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Reading books is a kind of enjoyment. Reading books is a good habit. We bring you a different kinds of books. You can carry this book where ever you want. It is easy to carry. It can be an ideal gift to yourself and to your loved ones.

Care instruction keep away from fire.

15. Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine Learning

Author: by Alex J. Gutman
Wiley
English
272 pages

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“Turn yourself into a Data Head. You’ll become a more valuable employee and make your organization more successful.” Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You’ve heard the hype around datanow get the facts.

In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.

You’ll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what’s really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms.

16. SQL Cookbook: Query Solutions and Techniques for All SQL Users

Author: by Anthony Molinaro
O'Reilly Media
English
572 pages

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You may know SQL basics, but are you taking advantage of its expressive power? This second edition applies a highly practical approach to Structured Query Language (SQL) so you can create and manipulate large stores of data. Based on real-world examples, this updated cookbook provides a framework to help you construct solutions and executable examples in severalflavors of SQL, including Oracle, DB2, SQL Server, MySQL, andPostgreSQL.

SQL programmers, analysts, data scientists, database administrators, and even relatively casual SQL users will find SQL Cookbook to be a valuable problem-solving guide for everyday issues. No other resource offers recipes in this unique format to help you tackle nagging day-to-day conundrums with SQL.

The second edition includes:Fully revised recipes that recognize the greater adoption of window functions in SQL implementationsAdditional recipes that reflect the widespread adoption of common table expressions (CTEs) for more readable, easier-to-implement solutionsNew recipes to make SQL more useful for people who aren’t database experts, including data scientistsExpanded solutions for working with numbers and stringsUp-to-date SQL recipes throughout the book to guide you through the basics