100+ Free Data Science Books (Updated for 2021) - Download Best eBooks for Free

Jan 28, 2021 0 comments
100+ Free Data Science Books

Hi Everyone! Before starting, let me ask you "Have you checked 100+ Free Machine Learning and Artificial Intelligence Books? If you missed it, make sure you spend 2 minutes to check that worthy article." Moving to the point, What's special today or in today's article? You might have this question in your mind. Don't worry, just keep your ears open and listen clearly. Today, I am going to share 100+ free data science books in this post. Some of this books are best selling books, some are worth reading, some are written by experts, and the special thing is all are available for free. Just download the pdf and start reading 

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👉 100+ Free Data Science Books

The Data Science Handbook

The Data Science Handbook PDF

Author: William Chen, Henry Wang, Carl Shan, Max Song

What's Special about this eBook:

The Data Science Handbook contains candid interviews with 25 of the world’s best data scientists.

This book contains insight and interviews with data scientists from established companies such as Facebook, LinkedIn, Pandora, Intuit, and The New York Times.

We also spoke with data scientists at fast-growing startups such as Uber, Airbnb, Mattermark, Quora, Square and Khan Academy

In The Data Science Handbook, You’ll learn from industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You’ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program.

READ HERE    OR    DOWNLOAD PDF

Python Data Science Handbook

Python Data Science Handbook PDF

Author: Jake VanderPlas 

What's Special about this eBook:

With this handbook, you’ll learn how to use:

- Python and Jupyter: provide computational environments for data scientists using Python

- NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python

- Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python

- Matplotlib: includes capabilities for a flexible range of data visualizations in Python

- Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

READ HERE    OR    DOWNLOAD PDF

The Elements of Statistical Learning: Data Mining, Inference, etc

The Elements of Statistical Learning: Data Mining, Inference, etc

Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman

What's Special about this eBook:

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.


You may like this: 100+ Free Programming Books

Introduction to Probability

Introduction to Probability by Charles PDF

Author: Charles M. Grinstead, J. Laurie Snell

What's Special about this eBook:

This text is designed for an introductory probability course taken by sophomores, juniors, and seniors in mathematics, the physical and social sciences, engineering, and computer science. It presents a thorough treatment of probability ideas and techniques necessary for a form understanding of the subject.


R for Data Science: Import, Tidy, Transform, Visualize, and Model Data 

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data PDF

Author: Garrett Grolemund and Hadley Wickham

What's Special about this eBook:

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

READ HERE    OR    DOWNLOAD PDF

Data-Intensive Text Processing with MapReduce

Data-Intensive Text Processing with MapReduce PDF

Author: Chris Dyer and Jimmy Lin

What's Special about this eBook:

This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.

READ HERE    OR    DOWNLOAD PDF

Text Mining with R: A Tidy Approach

Text Mining with R: A Tidy Approach PDF

Author: David Robinson and Julia Silge

What's Special about this eBook:

With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.

The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.



Foundations of Data Science

Foundations of Data Science PDF

Author: Avrim Blum, John Hopcroft, and Ravindran Kannan

What's Special about this eBook:

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, and more. 

This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.


Data Mining and Analysis: Fundamental Concepts and Algorithms

Data Mining and Analysis: Fundamental Concepts and Algorithms

Author: Mohammed J. Zaki

What's Special about this eBook:

This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.

READ HERE    OR    DOWNLOAD PDF

Genetic algorithms in search, optimization, and machine learning


Author: David E. Goldberg

What's Special about this eBook:

A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning.


Social Media Mining: An Introduction

Social Media Mining: An Introduction PDF

Author: Novel by Huan Liu, Mohammad Ali Abbasi, and Reza Zafarani

What's Special about this eBook:

Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.


Advanced R

Advanced R book PDF

Author: Hadley Wickham

What's Special about this eBook:

Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.

This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.

READ HERE    OR    DOWNLOAD PDF


Open Data Structures - An Introduction

Open Data Structures - An Introduction PDF

Author: Pat Morin

What's Special about this eBook:

Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruction along with source code.

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Think Python: How to Think Like a Computer Scientist

Think Python: How to Think Like a Computer Scientist PDF

Author: Allen B. Downey

What's Special about this eBook:

What You'll Learn

- Start with the basics, including language syntax and semantics
- Get a clear definition of each programming concept
- Learn about values, variables, statements, functions, and data structures in a logical progression
- Discover how to work with files and databases
- Understand objects, methods, and object-oriented programming
- Use debugging techniques to fix syntax, runtime, and semantic errors
- Explore interface design, data structures, and GUI-based programs through case studies

READ HERE    OR    DOWNLOAD PDF

Automate the Boring Stuff with Python: Practical Programming for Total Beginners

Automate the Boring Stuff with Python: Practical Programming for Total Beginners PDF

Author: Al Sweigart

What's Special about this eBook:

In Automate the Boring Stuff with Python, you’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand—no prior programming experience required. Once you’ve mastered the basics of programming, you’ll create Python programs that effortlessly perform useful and impressive feats of automation to:

- Search for text in a file or across multiple files
- Create, update, move, and rename files and folders
- Search the Web and download online content
- Update and format data in Excel spreadsheets of any size
- Split, merge, watermark, and encrypt PDFs
- Send reminder emails and text notifications
- Fill out online forms

READ HERE    OR    DOWNLOAD PDF

Introduction to Information Retrival

Introduction to Information Retrival PDF

Author: Christopher D. Manning

What's Special about this eBook:

This is the first book that gives you a complete picture of the complications that arise in building a modern web-scale search engine. You'll learn about ranking SVMs, XML, DNS, and LSI. You'll discover the seedy underworld of spam, cloaking, and doorway pages.

This textbook covers all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.

READ HERE    OR    DOWNLOAD PDF

ggplot2: Elegant Graphics for Data Analysis

ggplot2: Elegant Graphics for Data Analysis PDF

Author: Hadley Wickham

What's Special about this eBook:

This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R).  ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.


How to be a Modern Scientist

How to be a Modern Scientist PDF



Author: Jeff Leek

What's Special about this eBook:

The face of academia is changing. It is no longer sufficient to just publish or perish. We are now in an era where Twitter, Github, Figshare, and Alt Metrics are regular parts of the scientific workflow. Here I give high level advice about which tools to use, how to use them, and what to look out for. This book is appropriate for scientists at all levels who want to stay on top of the current technological developments affecting modern scientific careers.


D3 Tips and Tricks

D3 Tips and Tricks PDF

Author: Malcolm Maclean

What's Special about this eBook:

D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization. It's not written for experts. It's put together as a guide to get you started if you're unsure what d3.js can do. It reads more like a story as it leads the reader through the basics of line graphs and on to discover animation, tooltips, tables, interfacing with MySQL databases via PHP, sankey diagrams, force diagrams, maps and more...


Statistical Learning with Sparsity: The Lasso and Generalizations

Statistical Learning with Sparsity: The Lasso and Generalizations

Author: A. Martin Wainwright, Robert Tibshirani, and Trevor Hastie

What's Special about this eBook:

Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.


R Graphics Cookbook: Practical Recipes for Visualizing Data

R Graphics Cookbook: Practical Recipes for Visualizing Data PDF

Author: Winston Chang

What's Special about this eBook:

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.


Data Visualization: A Practical Introduction

Data Visualization: A Practical Introduction PDF

Author: Kieran Healy

What's Special about this eBook:

Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.



Modeling with Data: Tools and Techniques for Scientific Computing

Modeling with Data: Tools and Techniques for Scientific Computing

Author: Ben Klemens

What's Special about this eBook:

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.


Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Author: Cameron Davidson-Pilon 

What's Special about this eBook:

Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.


Data Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques PDF

Author:  Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal

What's Special about this eBook:

Data Mining: Practical Machine Learning Tools and Techniques, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.

READ HERE    OR    DOWNLOAD PDF

Advanced Statistics From an Elementary Point of View

Advanced Statistics From an Elementary Point of View PDF

Author: Michael J. Panik

What's Special about this eBook:

Advanced Statistics from an Elementary Point of View is a highly readable text that clearly emphasizes the connection between statistics and probability, and helps students concentrate on statistical strategies without being overwhelmed by calculations. 

This book is designed for statistics majors who are already familiar with introductory calculus and statistics, and can be used in either a one- or two-semester course. It can also serve as a statistics tutorial or review for working professionals.


A Programmer's Guide to Data Mining

A Programmer's Guide to Data Mining PDF

Author: Ron Zacharski

What's Special about this eBook:

This guide follows a learn-by-doing approach. You are encouraged to work through the exercises and experiment with the Python code provided.

The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques. This book is available for download for free under a Creative Commons license.


The Data Science Design Manual

The Data Science Design Manual PDF

Author: Steven Skiena

What's Special about this eBook:

The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.


Oracle Database Notes for Professionals

Oracle Database Notes for Professionals PDF

Author: The StackOverFlow Community

What's Special about this eBook:

This book is the definitive guide to undocumented and partially-documented features of the Oracle Database server. It helps you learn to apply the right solution at the right time, about avoiding risk, about making robust choices related to Oracle databases. It is packed with of experience over decades deep to lay out real-world techniques.


SQL Notes for Professionals

SQL Notes for Professionals PDF

Author: The StackOverFlow Community

What's Special about this eBook:

In the SQL Notes for Professionals, experienced SQL developers all over the world share their favorite SQL techniques and features. Learning how to solve real-world problems will give you the skill and confidence to step up in your career. The SQL Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow.


Ethics and Data Science

Ethics and Data Science PDF

Author: DJ Patil, Hilary Mason, and Mike Loukides

What's Special about this eBook:

With this eBook, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day.


MySQL Notes for Professionals

MySQL Notes for Professionals PDF

Author: The StackOverFlow Community

What's Special about this eBook:

MySQL's popularity has brought a flood of questions about how to solve specific problems, and that's where this MySQL Notes for Professionals is essential. When you need quick solutions or techniques, this handy resource provides scores of short, focused pieces of code, hundreds of worked-out examples, and clear, concise explanations for programmers who don't have the time (or expertise) to solve MySQL problems from scratch.


Linear Regression Using R: An Introduction to Data Modeling

Linear Regression Using R: An Introduction to Data Modeling PDF

Author: David J. Lilja

What's Special about this eBook:

Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.

READ HERE    OR    DOWNLOAD PDF

PostgreSQL Notes for Professionals

PostgreSQL Notes for Professionals book PDF

Author: The StackOverFlow Community

What's Special about this eBook:

This book is the definitive guide to undocumented and partially-documented features of the PostgreSQL server. It helps you learn to apply the right solution at the right time, about avoiding risk, about making robust choices related to PostgreSQL databases. It is packed with of experience over decades deep to lay out real-world techniques. PostgreSQL Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow.


Building Secure and Reliable Systems - Best Practices for Designing, Implementing, and Maintaining Systems

Building Secure and Reliable Systems - Best Practices for Designing, Implementing, and Maintaining Systems

Author: Heather Adkins, Ana Oprea, Paul Blankinship, Piotr Lewandowski, Adam Stubblefield, Betsy Beyer

What's Special about this eBook:

In this book, experts from Google share best practices to help your organization design scalable and reliable systems that are fundamentally secure. In this latest guide, the authors offer insights into system design, implementation, and maintenance from practitioners who specialize in security and reliability. They also discuss how building and adopting their recommended best practices requires a culture that's supportive of such change.


Think Bayes: Bayesian Statistics in Python

Think Bayes PDF

Author: Allen B. Downey

What's Special about this eBook:

With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems.

Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start.

READ HERE    OR    DOWNLOAD PDF

Think Stats: Exploratory Data Analysis in Python

Think Stats Book PDF

Author: Allen B. Downey

What's Special about this eBook:

By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.

New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.

READ HERE    OR    DOWNLOAD PDF

Statistical Inference for Data Science

Statistical Inference for Data Science PDF

Author: Brian Caffo

What's Special about this eBook:

This book is written as a companion book to the Statistical Inference Coursera class as part of the Data Science Specialization. However, if you do not take the class, the book mostly stands on its own. A useful component of the book is a series of YouTube videos that comprise the Coursera class.


The Element of Data Analytic Style

The Element of Data Analytic Style PDF

Author: Jeff Leek

What's Special about this eBook:

Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. It is based in part on the authors blog posts, lecture materials, and tutorials. 


Causal Inference

Causal Inference: What if PDF

Author: James Robins and Miguel Hernán

What's Special about this eBook:

The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.


Data Science: Theories, Models, Algorithms, and Analytics

Data Science: Theories, Models, Algorithms, and Analytics

Author: Sanjiv Ranjan Das

What's Special about this eBook:

Table of Contents:

- The Art of Data Science
- The Very Beginning: Got Math?
- Open Source Modeling in R
- More: Data Handling and Other Useful Things
- Being Mean with Variance: Markowitz Optimization
- Learning from Experience: Bayes Theorem
- More than Words: Extracting Information from News
- Virulent Products: thaw Bass Model
- Extracting Dimensions: Discriminant and Factor Analysis
- Bidding it Up: Auctions
- Truncate and Estimate: Limited Dependent Variables
- Riding the Wave: Fourier Analysis
- Making Connections: Networking Theory
- Statical Brains: Neural Networks
- Zero or One: Optimal Digital Portfolios 
- Against the Odds: the Mathematics of Gambling
- In the Same Boat: Cluster Analysis and Prediction Trees


Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery

Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery

Author: Graham J. Williams and Graham Williams

What's Special about this eBook:

This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built. This is the first step in a journey to data mining and analytics. The book encourages the concept of programming by example and programming with data - more than just pushing data through tools, but learning to live and breathe the data, and sharing the experience so others can copy and build on what has gone before


An Introduction to Data Science

An Introduction to Data Science by Jeffrey

Author: Jeffrey M. Stanton and Jeffrey S. Saltz

What's Special about this eBook:

An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data.



Data Jujitsu: The Art of Turning Data into Product

Data Jujitsu: The Art of Turning Data into Product

Author: DJ Patil

What's Special about this eBook:

- Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu.

- 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


The Art of Data Science

The Art of Data Science PDF

Author: Elizabeth Matsui and Roger D. Peng

What's Special about this eBook:

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.


Building Data Science Teams

Building Data Science Teams PDF

Author: DJ Patil

What's Special about this eBook:

Topics include: 

What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.


Data Driven: Creating a Data Culture

Data Driven: Creating a Data Culture PDF

Author: DJ Patil and Hilary Mason

What's Special about this eBook:

You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century.


R Programming for Data Science

R Programming for Data Science PDF

Author: Roger D. Peng

What's Special about this eBook:

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.


Executive Data Science - A Guide to Training and Managing the Best Data Scientists

Executive Data Science - A Guide to Training and Managing the Best Data Scientists

Author: Brian Caffo, Roger D. Peng, and Jeffrey Leek

What's Special about this eBook:

This book teaches you how to assemble and lead a data science enterprise so that your organization can move towards extracting information from big data. This book is based on the acclaimed Johns Hopkins Executive Data Science Specialization. 


Exploratory Data Analysis with R

Exploratory Data Analysis with R PDF

Author: Roger D. Peng

What's Special about this eBook:

This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

READ HERE    OR    DOWNLOAD PDF

OpenIntro Statistics, 4th Edition (2019)

OpenIntro Statistics, 4th Edition (2019) PDF

Author: by David Diez, Mine Çetinkaya-Rundel, Christopher Barr

What's Special about this eBook:

There is more than enough material for any introductory statistics course. There are a lot of topics covered. The topics are not covered in great depth; however, as an introductory text, it is appropriate. 


Data Science at the Command Line: Facing the Future with Time-Tested Tools

Data Science at the Command Line: Facing the Future with Time-Tested Tools

Author: Jeroen Janssens

What's Special about this eBook:

This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data



Theory and Applications for Advanced Text Mining

Theory and Applications for Advanced Text Mining

Author: Shigeaki Sakurai 

What's Special about this eBook:

This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.


Data Science: An Introduction (WikiBook)

Author: Wikibooks

What's Special about this eBook:

This book is a very basic introduction to data science. It is designed for the advanced high school student or average college freshman with a high school-level understanding of math, science, word processing and spreadsheets. No understanding of computer science is assumed. The main emphasis of this book is to help students think about the world in data science terms.


Disruptive Possibilities: How Big Data Changes Everything

Disruptive Possibilities: How Big Data Changes Everything

Author: Jeffrey Needham

What's Special about this eBook:

Disruptive Possibilities provides an historically-informed overview through a wide range of topics, from the evolution of commodity supercomputing and the simplicity of big data technology, to the ways conventional clouds differ from Hadoop analytics clouds. This relentlessly innovative form of computing will soon become standard practice for organizations of any size attempting to derive insight from the tsunami of data engulfing them.


Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics

Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics



Author: David M. Smith and William N. Venables

What's Special about this eBook:

This eBook provides a comprehensive introduction to R, a software package for statistical computing and graphics. R supports a wide range of statistical techniques and is easily extensible via user-defined functions. One of R's strengths is the ease with which publication-quality plots can be produced in a wide variety of formats. This is a printed edition of the tutorial documentation from the R distribution, with additional examples, notes and corrections.

READ HERE    OR    DOWNLOAD PDF

Fundamental Numerical Methods and Data Analysis 

Fundamental Numerical Methods and Data Analysis PDF

Author: George W. Collins

What's Special about this eBook:

The basic premise of this book is that it can serve as the basis for a wide range of courses that discuss numerical methods used in data analysis and science. It is meant to support a series of lectures, not replace them. To reflect this, the subject matter is wide ranging and perhaps too broad for a single course.

READ HERE    OR    DOWNLOAD PDF

Introduction to Social Network Methods 

Introduction to Social Network Methods

Author: Robert Hanneman, Mark Riddle

What's Special about this eBook:

This textbook introduces many of the basics of formal approaches to the analysis of social networks. The text relies heavily on the work of Freeman, Borgatti, and Everett (the authors of the UCINET software package). The materials here, and their organization, were also very strongly influenced by the text of Wasserman and Faust, and by a graduate seminar conducted by Professor Phillip Bonacich at UCLA. Many other users have also made very helpful comments and suggestions based on the first version.

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Analyzing Linguistic Data: a practical introduction to statistics 

Analyzing Linguistic Data: a practical introduction to statistics

Author: R. H. Baayan

What's Special about this eBook:

This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. 


Applied Data Science 

Applied Data Science Book PDF

Author: Ian Langmore

What's Special about this eBook:

"Applied Data Science" is a free data science book that focuses more on the statistics end of things, while also getting readers going on (basic) programming & command line skills. It doesn't, however, really go into much of the stuff you would expect to see from the machine learning end of things. But that's OK; there are other book for that. And this book (Applied Data Science) is worth a read for the topics it does cover.



Introduction to Statistical Thought 

Introduction to Statistical Thought PDF

Author: Michael Lavine

What's Special about this eBook:

This free PDF textbook is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking. It is best suited to students with a good knowledge of calculus and the ability to think abstractly. The focus of the text is the ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. Another unusual aspect is the use of statistical software as a pedagogical tool. 


Data Mining and Knowledge Discovery in Real Life Applications

Data Mining and Knowledge Discovery in Real Life Applications PDF

Author:  Julio Ponce 

What's Special about this eBook:

This book presents four different ways of theoretical and practical advances and applications of data mining in different promising areas like Industrialist, Biological, and Social. Twenty six chapters cover different special topics with proposed novel ideas. Each chapter gives an overview of the subjects and some of the chapters have cases with offered data mining solutions. We hope that this book will be a useful aid in showing a right way for the students, researchers and practitioners in their studies.


The SysAdmin Handbook

The SysAdmin Handbook PDF

Author: Various

What's Special about this eBook:

Authors have brought the best articles together to form The SysAdmin Handbook. With over fifty articles packed into this book, it will be an essential reference for any Systems Administrator, whether you have years of experience or are just starting out.


Knowledge-Oriented Applications in Data Mining 

Author: Kimito Funatsu

What's Special about this eBook:

This book is a complete and comprehensive handbook for the application of data mining techniques in marketing and customer relationship management. It combines a technical and a business perspective, bridging the gap between data mining and its use in marketing.


R and Data Mining: Examples and Case Studies

R and Data Mining: Examples and Case Studies PDF

Author: Yanchang Zhao

What's Special about this eBook:

The book helps researchers in the field of data mining, postgraduate students who are interested in data mining, and data miners and analysts from industry. For the many universities that have courses on data mining, this book is an invaluable reference for students studying data mining and its related subjects.


Conversations On Data Science

Conversations On Data Science Book PDF

Author: Roger D. Peng and Hilary Parker

What's Special about this eBook:

Roger Peng and Hilary Parker started the Not So Standard Deviations podcast in 2015, a podcast dedicated to discussing the backstory and day to day life of data scientists in academia and industry. This book collects many of their conversations about data science and how it works (and sometimes doesn’t work) in the real world.


Advanced Linear Models for Data Science

Advanced Linear Models for Data Science

Author: Brian Caffo

What's Special about this eBook:

In this book, Authors give a brief, but rigorous treatment of advanced linear models. It is advanced in the sense that it is of level that an introductory PhD student in statistics or biostatistics would see. The material in this book is standard knowledge for any PhD in statistics or biostatistics. 


Big Data, Data Mining, and Machine Learning

Big Data, Data Mining, and Machine Learning PDF

Author: Jared Dean

What's Special about this eBook:

Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency.


Inductive Logic Programming: Techniques and Applications 

Inductive Logic Programming: Techniques and Applications PDF

Author: Nada Lavrac

What's Special about this eBook:
 
This book is an introduction to inductive logic programming (ILP), a research field at the intersection of machine learning and logic programming, which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs.

The book extensively covers empirical inductive logic programming, one of the two major subfields of ILP, which has already shown its application potential in the following areas: knowledge acquisition, inductive program synthesis, inductive data engineering, and knowledge discovery in databases.


The Field Guide of Data Science

The Field Guide of Data Science PDF

Author: Booz Allen Hamilton

What's Special about this eBook:

The Field Guide to Data Science spells out what data science is, why it matters to organizations, as well as how to create data science teams. Along the way, our team of experts provides field-tested approaches, personal tips and tricks, and real-life case studies. Senior leaders will walk away with a deeper understanding of the concepts at the heart of data science, practitioners will add to their toolboxes, and beginners will find insights to help them start on their data science journey.


Modern Data Science for Modern Biology 

Author: Susan Holmes

What's Special about this eBook:

This book will teach you 'cooking from scratch', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology.


Crash Course on Basic Statistics (PDF)

Crash Course on Basic Statistics PDF

Author: Marina Wahl

What's Special about this eBook:

A Crash Course in Statistics by Ryan J. Winter is a short introduction to key statistical methods including descriptive statistics, one-way and two-way ANOVA, the t-test, and Chi Square. Each of the five chapters provides an overview of each method, and then walks readers through a relevant example, using SPSS to highlight how to run the statistics and how to write up the results in APA style. Each chapter ends with a self-quiz so that readers can assess their understanding of each statistical concept.


Hands-on Machine Learning and Big Data

Author: Kareem Alkaseer

What's Special about this eBook:

What you will learn?

- Learn how to clean your data and ready it for analysis
- Implement the popular clustering and regression methods in Python
- Train efficient machine learning models using decision trees and random forests
- Visualize the results of your analysis using Python’s Matplotlib library
- Use Apache Spark’s MLlib package to perform machine learning on large datasets


Mathematical Foundations of Data Science

Mathematical Foundations of Data Science PDF

Author: Gabriel PeyrĂ©

What's Special about this eBook:

This book presents an overview of important mathematical and numerical foundations for modern data sciences. In particular, it covers the basics of signal and image processing (Fourier, Wavelets, and their applications to denoising and compression), imaging sciences (inverse problems, sparsity, compressed sensing) and machine learning (linear regression, logistic classification, deep learning). The focus is on the mathematically-sound exposition of the methodological tools (in particular linear operators, non-linear approximation, convex optimization, optimal transport) and how they can be mapped to efficient computational algorithms.


Scipy Lecture Notes

Scipy Lecture Notes PDF

Author: Scipy  Lectures

What's Special about this eBook:

Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert.


Statistics With Julia

Statistics With Julia PDF

Author: Yoni Nazarathy and Hayden Klok

What's Special about this eBook:

The book's table of contents, including appendices:

- Introducing Julia
- Basic Probability
- Probability Distributions
- Processing and Summarizing Data
- Statistical Inference Concepts
- Confidence Intervals
- Hypothesis Testing
- Linear Regression and Extensions
- Machine Learning Basics
- Simulation of Dynamic Models


A Genetic Algorithm Tutorial

Author: Darrell Whitley

What's Special about this eBook:

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.


Become a Leader in Data Science

Become a Leader in Data Science - Early access

Author: Jike Chong and Yue Cathy Chang

What's Special about this eBook:

In Become a Leader in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away.


Exploring Data with Python

Exploring Data with Python PDF

Author: Naomi Ceder

What's Special about this eBook:

Exploring Data with Python is a collection of chapters from three Manning books, hand-picked by Naomi Ceder, the chair of the Python Software Foundation. This free eBook starts building your foundation in data science processes with practical Python tips and techniques for working and aspiring data scientists.


Understanding Databases

Understanding Databases PDF

Author: David Clinton

What's Special about this eBook:

In this book, you’ll learn about database configuration, how to assess database storage, and how and why to move or copy your database. As you look at relational databases and infrastructure design, you’ll also discover the factors to consider when choosing your database architecture and explore illuminating real-world case studies that shine a light on NoSQL databases and the elements that drive NoSQL business solutions. This on-point guide is a great way to start learning about databases, how to use them, and how to choose the right one for your tasks.


Think Like a Data Scientist

Think Like a Data Scientist PDF

Author: Brian Godsey

What's Special about this eBook:

Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice.


Exploring Streaming Data Analysis

Exploring Streaming Data Analysis PDF

Author: Alexander Dean

What's Special about this eBook:

You’ll learn the algorithmic side of stream processing, focusing on the what and why of streaming analysis algorithms. You’ll cover common constraints, approaches for thinking about time, and techniques for summarization. Finally, you’ll take a look at how the Kafka Streams framework uses local state to extract the maximum amount of information from event streams. This mini ebook provides the well-rounded introduction you need to get up to speed in the basics of streaming data analysis!


Exploring Data Science

Exploring Data Science PDF

Author: John Mount and Nina Zumel

What's Special about this eBook:

Exploring Data Science is a collection of five hand-picked chapters introducing you to various areas in data science and explaining which methodologies work best for each. John Mount and Nina Zumel, authors of Practical Data Science with R, selected these chapters to give you the big picture of the many data domains. You’ll learn about time series, neural networks, text analytics, and more.


Practical Data Science with R

Practical Data Science with R PDF

Author: John Mount and Nina Zumel

What's Special about this eBook:

Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.


Exploring the Data Jungle 

Exploring the Data Jungle PDF

Author: Brian Godsey

What's Special about this eBook:

Exploring the Data Jungle: Finding, Preparing, and Using Real-World Data is a collection of three hand-picked chapters introducing you to the often-overlooked art of putting unfamiliar data to good use. Brian Godsey, author of Think Like a Data Scientist, has selected these chapters to help you navigate data in the wild, identify and prepare raw data for analysis, modeling, machine learning, or visualization. As you explore the data jungle you'll discover real-world examples in Python, R, and other languages suitable for data science.


Exploring Math for Programmers and Data Scientists

Exploring Math for Programmers and Data Scientists PDF

Author: Paul Orland

What's Special about this eBook:

You’ll start with a look at the nearest neighbor search problem, common with multidimensional data, and walk through a real-world solution for tackling it. Next, you’ll delve into a set of methods and techniques integral to Principal Component Analysis (PCA), an underlying technique in Latent Semantic Analysis (LSA) for document retrieval. In the last chapter, you’ll work with digital audio data, using mathematical functions in different and interesting ways. 


Advances in Evolutionary Algorithms

Author: Witold Kosinski

What's Special about this eBook:

Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. 


Genetic Programming: New Approaches and Successful Applications

Genetic Programming: New Approaches and Successful Applications

Author: Sebastian Ventura

What's Special about this eBook:

The purpose of this book is to show recent advances in the field of GP, both the development of new theoretical approaches and the emergence of applications that have successfully solved different real world problems. The volume is primarily aimed at postgraduates, researchers and academics, although it is hoped that it may be useful to undergraduates who wish to learn about the leading techniques in GP.


Global Optimization Algorithms: Theory and Application

Global Optimization Algorithms: Theory and Application

Author: Thomas Weise

What's Special about this eBook:

This book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on Evolutionary Computation by dis- cussing evolutionary algorithms, genetic algorithms, Genetic Programming, Learning Classi- fier Systems, Evolution Strategy, Differential Evolution, Particle Swarm Optimization, and Ant Colony Optimization. It also elaborates on other metaheuristics like Simulated An- nealing, Extremal Optimization, Tabu Search, and Random Optimization.


Algorithms Notes for Professionals

Algorithms Notes for Professionals PDF

Author: The Stackoverflow Community

What's Special about this eBook:

The Algorithms Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow.


Graph Databases

Graph Databases PDF

Author: Ian Robinson

What's Special about this eBook:

Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.


Regression Models for Data Science in R

Regression Models for Data Science in R PDF

Author: Brian Caffo

What's Special about this eBook:

The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming. The student should have a basic understanding of statistical inference such as contained in "Statistical inference for data science". The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. After reading the book and watching the associated videos, students will be able to perform multivariable regression models and understand their interpretations.


Think Data Structures

Think Data Structures PDF

Author: Allen Downey

What's Special about this eBook:

If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials.


Excel Data Analysis 

Excel Data Analysis PDF

Author: Hector Guerrero

What's Special about this eBook:

This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are focused on understanding the techniques and their proper application, and are supplemented by a wealth of in-chapter and end-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization.


Data Visualization in Society

Data Visualization in Society PDF

Author: Martin Engebretsen, Helen Kennedy

What's Special about this eBook:

In an era in which more and more data are produced and circulated digitally, and digital tools make visualization production increasingly accessible, it is important to study the conditions under which such visual texts are generated, disseminated and thought to be of societal benefit. This book is a contribution to the multi-disciplined and multi-faceted conversation concerning the forms, uses and roles of data visualization in society. Do data visualizations do 'good' or 'bad'? Do they promote understanding and engagement, or do they do ideological work, privileging certain views of the world over others? The contributions in the book engage with these core questions from a range of disciplinary perspectives


SQL Server Backup and Restore

SQL Server Backup and Restore PDF

Author: Shawn McGehee

What's Special about this eBook:

In this book, you'll discover how to perform each of these backup and restore operations using SQL Server Management Studio (SSMS), basic T-SQL scripts and Red Gate's SQL Backup tool. Capturing backups using SSMS or simple scripts is perfectly fine for one-off backup operations, but any backups that form part of the recovery strategy for any given database must be automated and you'll also want to build in some checks that, for example, alert the responsible DBA immediately if a problem arises. The tool of choice in this book for backup automation is Red Gate SQL Backup. Building your own automated solution will take a lot of work, but we do offer some advice on possible options, such as PowerShell scripting, T-SQL scripts and SQL Server Agent jobs.


Making Sense of Stream Processing: Behind Apache Kafka

Making Sense of Stream Processing: Behind Apache Kafka

Author: Martin Kleppmann

What's Special about this eBook:

This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. Structuring data as a stream of events isn't new, but with the advent of open source projects such as Apache Kafka and Apache Samza, stream processing is finally coming of age.

Using several case studies, it explains how these projects can help you reorient your database architecture around streams and materialized views. The benefits of this approach include better data quality, faster queries through precomputed caches, and real-time user interfaces. Learn how to open up your data for richer analysis and make your applications more scalable and robust in the face of failures.


Machine Learning for Data Streams: Practical Examples in MOA

Machine Learning for Data Streams: Practical Examples in MOA

Author: Geoff Holmes, Ricard GavaldĂ , Albert Bifet, Bernhard Pfahringer

What's Special about this eBook:

This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.


Just Enough R: Learn Data Analysis with R in a Day

Just Enough R: Learn Data Analysis with R in a Day

Author: S. Raman

What's Special about this eBook:

Learn R programming for data analysis in a single day. The book aims to teach data analysis using R within a single day to anyone who already knows some programming in any other language. 

This book has been crafted in a step-by-step manner which we feel is the best way for you to learn a new subject, one step at a time. It also includes various images to give you assurance you are going in the right direction, as well as having exercises where you can proudly practice your newly attained skills.

READ HERE    OR    DOWNLOAD PDF

Data Blending For Dummies

Data Blending For Dummies PDF

Author: Michael Wessler

What's Special about this eBook:

This book helps you understand the benefits of data blending, and see how to build the data set you need to meet your organization's analytical needs, without writing scripts or waiting on other departments.

Read this book to learn how to:

- Access, cleanse, and join data in any format from your hard drive, data warehouses, social media, and more
- Prepare data for reports, presentations, visualization, or export to feed downstream processes
- Create an intuitive workflow to document and automate data manipulation tasks


Data Mining Applications in Engineering and Medicine

Data Mining Applications in Engineering and Medicine

Author: Adem Karahoca

What's Special about this eBook:

In this book, most of the areas are covered by describing different applications. This is why you will find here why and how Data Mining can also be applied to the improvement of project management. Since Data Mining has been widely used in a medical field, this book contains different chapters reffering to some aspects and importance of its use in the mentioned field: Incorporating Domain Knowledge into Medical Image Mining, Data Mining Techniques in Pharmacovigilance, Electronic Documentation of Clinical Pharmacy Interventions in Hospitals etc. 


Understanding Big Data: Analytics for Hadoop and Streaming Data

Understanding Big Data: Analytics for Hadoop and Streaming Data PDF

Author: Chris Eaton and Paul C. Zikopoulos

What's Special about this eBook:

The three defining characteristics of Big Data--volume, variety, and velocity--are discussed. You'll get a primer on Hadoop and how IBM is hardening it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Industry use cases are also included in this practical guide.


Applied Spatial Data Analysis with R 

Applied Spatial Data Analysis with R PDF

Author: Edzer J. Pebesma, Roger Bivand, and Virgilio Gomez-Rubio

What's Special about this eBook:

This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science.


Do you like this huge list of free data science books? If yes, then without blinking an eye, use 5 second rule and decide whether to share this article or not. I know your mind will say yes. So, just hit the below share buttons and forward this list to other curious learners. And stay tuned with us because our next article will be on 100+ Data Science and Machine Learning Projects for Beginners, Intermediate and Advanced level Learners To give a small boost to your knowledge and skills. I will try my best to provide the codes, datasets, and other possible resources required in these projects.

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