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Links to Free Computer, Mathematics, Technical Books all over the World



Introduction to Statistics and Data Analysis: A CaseBased Approach
This short book is a complete introduction to statistics and data analysis using R and RStudio. It contains handson exercises with real data  mostly from social sciences. It presents four key ingredients of statistical data analysis.

Introduction to Statistics and Data Analysis (Geoffrey M. Boynton)
Build a solid foundation in data analysis, This guide starts with an overview of statistics and why it is so important. Be confident that you understand what your data are telling you and that you can explain the results to others!

Data Analysis with Python (Numpy, Matplotlib and Pandas)
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide, using Python. Equipped with the skills to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

An Introduction to Spatial Data Analysis and Statistics in R
This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for collecting and using data with location attached.

An Introduction to Spatial Data Analysis and Visualization in R
This book provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.

Data Mining with R: Learning with Case Studies (Luis Torgo)
Introduce the reader to the use of R as a tool for performing data mining and statistical computing and graphics. The large set of available packages make this tool an excellent alternative to the existing (and expensive!) data mining tools.

An Introduction to R and Python for Data Analysis
This book helps teach students to code in both R and Python simultaneously. The book is written in an engaging, collaborative style that makes it enjoyable to read. It maintains its formality without creating a barrier between the reader and the content.

Python for Data Analysis: Pandas, NumPy, and Jupyter
The focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.

Python for Econometrics, Statistics, and Data Analysis
This book is designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research for econometrics, statistics or general numerical analysis using Python.

Analyzing US Census Data: Methods, Maps, and Models in R
This book introduces readers to tools in the R programming language for accessing and analyzing Census data from the United States Census Bureau and shows how to carry out demographic analyses in a single computing environment.

The Fundamentals of People Analytics: With Applications in R
Human capital is an organizationâ€™s most important asset. Address this need by curating key concepts spanning the entire analytics lifecycle, along with stepbystep instructions for their applications to realworld problems, using opensource software.

Introduction to Statistical Data Analysis with R (Matthias Kohl)
The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data.

Metalearning: Applications to AutoML and Data Mining
This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and Automated Machine Learning (AutoML). It can help developers to develop systems that can improve themselves through experience.

Kafka: The Definitive Guide: RealTime Data and Stream Processing
Through detailed examples, you'll learn Kafka's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.

Making Sense of Stream Processing: Behind Apache Kafka
This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. It explains how these projects can help you reorient your database architecture around streams and materialized views.

HandsOn Data Visualization: From Spreadsheets to Code
This book takes you stepbystep through tutorials, realworld examples, and online resources. This practical guide is ideal for anyone who wants to take data out of spreadsheets and turn it into lively interactive stories.

Fundamentals of Data Visualization: Informative Figures
This book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures, teaches you the elements most critical to successful data visualization.

Data Visualization: A Practical Introduction (Kieran Healy)
It provides students and researchers a handson introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make highquality figures from data using powerful and reproducible methods.

Scientific Visualisation: Python and Matplotlib (Nicolas P. Rougier)
Matplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Through practical, handson and straightforward examples, the book guides you through Data Visualization and Exploration using Python and Matplotlib.

Advanced Data Analysis from an Elementary Point of View
This is a textbook on data analysis methods, intended for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It presumes that you can read and write simple functions in R.

Mining of Massive Datasets (Jure Leskovec, et al)
It focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to the largest datasets. It begins with a discussion of the mapreduce framework, an important tool for parallelizing algorithms automatically.

Spectral Feature Selection for Data Mining (Zheng A. Zhao, et al.)
This book introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in realworld applications.

Forecasting: Principles and Practice (Rob J. Hyndman, et al.)
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

Bayesian Data Analysis (Andrew Gelman, et al.)
This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using uptodate Bayesian methods.

Data Mining and Analysis: Fundamental Concepts and Algorithms
This textbook provides a broad yet indepth 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.

Answering Questions with Data (Matthew Crump, et al.)
This is a free textbook teaching introductory statistics for undergraduates. Students will learn to select an appropriate data analysis technique, carry out the analysis, and draw appropriate conclusions.

Exploratory Data Analysis with R (Roger D. Peng)
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.

Basic Data Analysis and More  A Guided Tour using Python
In this book, a selection of frequently required statistical tools will be introduced and illustrated. An exemplary implementation of the presented techniques using the Python programming language is provided.

Text Mining with R: A Tidy Approach (Julia Silge, et al)
You'll explore textmining techniques with tidytext, a package that authors 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.

Theory and Applications for Advanced Text Mining (S. Sakurai)
This book introduces advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. Text mining techniques have been studied aggressively in order to extract the knowledge from the data.

Text Processing in Python (David Mertz)
This book is an exampledriven, handson tutorial that carefully teaches programmers how to accomplish numerous text processing tasks using the Python language. It provides efficient and effective solutions to specific text processing problems.

Mining Social Media: Finding Stories in Internet Data
This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the Pandas library.

Data Mining for the Masses (Matthew North)
This book uses simple examples, clear explanations and free, powerful, easytouse software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions.

A Programmer's Guide to Data Mining (Ron Zacharski)
This book is a tool for learning basic data mining techniques. If you are a programmer interested in learning a bit about data mining you might be interested in a beginner's handson guide as a first step. That's what this book provides.

An Introduction to Data Mining (Dr. Saed Sayad)
This book presents fundamental concepts and algorithms for those learning data mining for the first time, provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures.

Knowledge Graphs and Big Data Processing (Valentina Janev, et al)
Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions.

The Promise and Peril of Big Data (David Bollier)
This book explores the positive aspects and the social perils that arise when the everrising floods of data being generated by mobile networking, cloud computing and other new technologies meets continued innovations in advanced correlation techniques.

Data Mining in Medical and Biological Research
This book intends to bring together the most recent advances and applications of data mining research in the promising areas of medicine and biology from around the world. It has twelve chapters related to medical research and five focused on the biological domain.

Machine Learning for Data Streams: Practical Examples in MOA
This book presents algorithms and techniques used in data stream mining and realtime analytics. Taking a handson approach, it demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available opensource software framework, allowing readers to try out the techniques after reading the explanations.

Just Enough R: Learn Data Analysis with R in a Day (S. Raman)
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.

Linear Regression Using R: An Introduction to Data Modeling
This book 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 stepbystep process to develop, train, and test reliable regression models.

The Data Science Handbook: Advice and Insights
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.

Exploring Data Science (Nina Zumel, et al)
This book introduces readers to various areas in data science and explains which methodologies work best for each, with practical examples in R, Python, and other languages.

Mastering Apache Spark 2.0 (Jacek Laskowski)
This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala.

Big Data on RealWorld Applications (Sebastian Ventura Soto)
The aim of this book is to provide the reader with a variety of fields and systems where the analysis and management of Big Data are essential. It describes the importance of the Big Data era and how existing information systems are required to be adapted.

Data Assimilation: A Mathematical Introduction (Kody Law, et al)
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework of Bayesian formulation.

Introduction to Data Science (Jeffrey Stanton)
This book provides nontechnical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics.

Analyzing Linguistic Data: Introduction to Statistics using R
A straightforward introduction to the statistical analysis of language, designed for those with a nonmathematical background. Using the leading statistics programme 'R'. Suitable for all those working with quantitative language data.

Python Scripting for Spatial Data Processing (Pete Bunting, et al)
This book is a Python tutorial for beginners aiming at teaching spatial data processing. It is used as part of the courses taught in Remote Sensing and GIS, using psycopg2, and ogr2ogr, etc., at Aberystwyth University, UK.

Think Stats, 2nd Edition: Exploratory Data Analysis in Python
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

School of Data Handbook
This textbook will provide the detail and background theory to support the Data Science courses and challenges. It will guide you through the key stages of a data project. These stages can be thought of as a pipeline, or a process.

Twitter Data Analytics (Shamanth Kumar, et al)
This book provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter's APIs and offers strategies for curating large datasets.

Machine Learning and Data Mining (Aaron Hertzmann)
This is an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining. It offers a grounding in machine learning concepts as well as practical advice on techniques in realworld data mining.

Social Media Mining: An Introduction (Reza Zafarani, et al)
This textbook introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.

O'Reilly® Bioinformatics Data Skills (Vince Buffalo)
This practical book teaches the skills that scientists need for turning large sequencing datasets into reproducible and robust biological findings. It demsonstrates the practice of bioinformatics through data skills.

Mining the Web: Discovering Knowledge from Hypertext Data
This is is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexing.

Data Blending For Dummies (Michael Wessler)
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.

Hadoop Succinctly (Elton Stoneman)
This booky explains how Hadoop works, what goes on in the cluster, demonstrates how to move data in and out of Hadoop, and how to query it efficiently. It also walks through a Java MapReduce example, illustrates it in Python and .NET, too.

Hadoop Illuminated (Mark Kerzner, et al)
This book aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical. It book introduces you to Hadoop and to concepts such as 'MapReduce', etc., which will help you get acquainted with the technology.

Disruptive Possibilities: How Big Data Changes Everything
This book takes you on a journey of discovery into the emerging world of big data, from its relatively simple technology to the ways it differs from cloud computing. It provides an historicallyinformed overview through a wide range of topics.

O'Reilly® Big Data Now: Current Perspectives from O'Reilly Radar
This book represents the full spectrum of datarelated content we've published on O'Reilly Radar over the last year.

Python for Econometrics, Statistics and Data Analysis
This book provides an introduction to Python for a beginning programmer. They may also be useful for an experienced Python programmer interested in using NumPy, SciPy, and matplotlib for numerical and statistical analaysis.

O'Reilly® Mining the Social Web, 2nd Edition (Matthew A. Russell)
This book shows you how to answer these questions like how can you tap into social data and discover who's connecting with whom, which insights are lurking just beneath the surface, and what people are talking about?

Statistical Methodologies and Their Application to Real Problems
This book provides a crossdisciplinary forum for exploring the variety of new data analysis techniques emerging from different fields, focusing on recent advances in data analysis techniques in many different research fields.

New Fundamental Technologies in Data Mining (Kimito Funatsu)
The book thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions.

Fundamental Numerical Methods and Data Analysis (G. W. Collins)
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.

O'Reilly® Agile Data: Building Data Analytics Applications
Create an environment for exploring data, using lightweight tools such as Ruby, Python, Apache Pig, and the D3.js (DataDriven Documents) JavaScript library. Learn an iterative approach that allows you to quickly change the kind of analysis you're doing.

Using R for Data Analysis and Graphics (J H Maindonald)
This book guides users through the practical, powerful tools that the R system provides. The emphasis is on handson analysis, graphical display, and interpretation of data.

Advances in Data Mining Knowledge Discovery and Applications
This book aims to help data miners, researchers, scholars, and students who wish to apply data mining techniques.

Getting Started with Data Warehousing (Neeraj Sharma, et al)
This book is for enthusiasts of data warehousing who have limited exposure to databases and would like to learn data warehousing concepts endtoend.

An Introduction to R: A Programming Environment for Data Analysis
This tutorial manual provides a comprehensive introduction to R, an open source software package for statistical computing and graphics.

Data Mining Applications in Engineering and Medicine
This book targets to help data miners who wish to apply different data mining techniques, including statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, etc.

O'Reilly® The Data Journalism Handbook (Jonathan Gray, et al.)
This book is intended to be a useful resource for anyone who thinks that they might be interested in becoming a data journalist, or dabbling in Data Dournalism, aims to answer questions like: Where can I find data? How can I request data? etc.

Understanding Big Data: Analytics for Hadoop and Streaming Data
In this free book, the three defining characteristics of Big Data  volume, variety, and velocity, are discussed. Industry use cases are also included in this practical guide.

DataIntensive Text Processing with MapReduce (Jimmy Lin)
This free book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.

O'Reilly® Planning for Big Data: Changing Data Landscape
Provides an efficient, userfriendly 'brief' on the current status of Big Data analytics and how you can economically deploy this technology to increase your firm's profitability.

KnowledgeOriented Applications in Data Mining (Kimito Funatsu)
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.

The Elements of Statistical Learning: Data Mining, Inference, etc.
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Introducing Microsoft Azure HDInsight  Technical Overview
This book covers what big data really means, how you can use it to your advantage in your company or organization, and one of the services you can use to do that quickly. Especifically, Microsoft's HDInsight service.

HDInsight Succinctly (James Beresford)
Learn how to set up and manage HDInsight clusters on Azure, how to use Azure Blob Storage to store input and output data, connect with Microsoft BI, and much more. It will reveal a new avenue of data management.

Large Scale Data Handling in Biology (Karol Kozak)
The book covers the data storage system, computational approaches to biological problems, an introduction to workflow systems, data mining, data visualization, and tips for tailoring existing data analysis software to individual research needs.

Python for Informatics: Exploring Information (Severance)
The primary difference between a computer science approach and the Informatics approach taken in this book is a greater focus on using Python to solve problems.

Data Mining Desktop Survival Guide (Graham William)
This book thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. Assuming no prior knowledge of R or data mining/statistical techniques.

The Fourth Paradigm: DataIntensive Scientific Discovery
This book presents the first broad look at the rapidly emerging field of dataintensive science, with the goal of influencing the worldwide scientific and computing research communities and inspiring the next generation of scientists.

Getting Started with Data Warehousing (Neeraj Sharma, et al)
This book is for enthusiasts of data warehousing who have limited exposure to databases and would like to learn data warehousing concepts endtoend. Learn what Data Warehousing is all about and practice using handson exercises.

Modeling with Data: Tools and Techniques for Scientific Computing
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, etc..

Data Mining and Knowledge Discovery in Real Life Applications
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 Networks..

Solving Problems With Visual Analytics (Daniel Keim, et al)
The aim is to outline the current state of Visual Analytics across many disciplines, and to describe the next steps that have to be taken to foster a strong visual analytics community, thus enabling the development of advanced visual analytic applications.

Document Image Analysis (Rangachar Kasturi)
This book describes some of the technical methods and systems used for document processing of text and graphics images.

Applied Spatial Data Analysis with R (Roger S. Bivand, et al)
It presents R packages, functions, classes and methods for handling spatial data, and showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping.

Statistical Foundations of Machine Learning using R
This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

Introduction to Metadata: 3rd Edition (Murtha Baca, et al)
Provides an overview of Metadata  its types, roles, and characteristics; a discussion of metadata as it relates to resources on the Web; a description of methods, tools, standards, and protocols that can be used to publish and disseminate digital collections;

Describing Data Patterns: A Deconstruction of Metadata Standards
It analyzes the methods, technologies, standards, and languages to structure and describe data in their entirety. It reveals common features, hidden assumptions, and ubiquitous patterns among these methods and shows how data are actually structured and described.

MultiRelational Data Mining (Arno Jan Knobbe)
This book goes into the different uses of Data Mining, with MultiRelational Data Mining (MRDM), the approach to Structured Data Mining, as the main subject of this book..

The Global Impact of Open Data: Key Findings from Case Studies
Open data has spurred economic innovation, social transformation, etc. This book presents detailed case studies of open data projects throughout the world, along with indepth analysis of what works and what doesn't.
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