Practical Data Processing and Analysis Using R

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.Data scien

This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent cas

Globally, a wide variety of organizations rely on ERDAS IMAGINE® daily, including local, state and national mapping agencies, transportation departments, defense organizations, engineering and utility

The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been over

The book presents the basic principles of sound graphical design and applies these principles to engaging examples using the graphical functions available in R. It offers a wide variety of graphical d

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be fou

Presenting in depth discussions of various statistical models and methods needed for the analysis of medical, biological and biostatistics data, this book emphasizes the analysis of data when normal d

"This textbook will introduce exploratory data analysis (EDA) and will cover the range of interesting features we can expect to find in data. The book will also explore the practical mechanics of usin

Gain the basics of Ruby’s map, reduce, and select functions and discover how to use them to solve data-processing problems. This compact hands-on book explains how you can encode certain complex progr

Qualitative and Mixed Methods Data Analysis Using Dedoose®: A Practical Approach for Research Across the Social Sciences provides both new and experienced researchers with a guided introduction to dea

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its s

Almost 50 years have passed since the famous papers of Hugo Rietveld from the late sixties where he describes a method for the refinement of crystal structures from neutron powder diffraction data. So

An open and dynamic software for statistical analysis, R has become increasingly popular among students and researchers alike for its powerful language and graphical abilities. This book inc

Chromatin immunoprecipitation sequencing (ChIP-seq), which maps the genome-wide localization patterns of transcription factors and epigenetic marks, is among the most widely used methods in molecular

This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, a

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human exper

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how t

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how t

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathe

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data sci

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for bot

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapt

Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are

本書《R語言與資料分析實戰》以R語言的「程式設計屬性」為中心，內容涵蓋R語言基礎理論到實際資料分析，透過分析模型和演算法等更實用的範例，講解了資料視覺化、統計分析、資料採擷、機器學習等常用的方法。同時書中還收錄了作者的實戰經驗和學習體會，可以解決資料統計分析過程中出現的各種問題。如果你是一名資料統計分析的研究人員，本書將是一本不可多得的參考書，能夠深化理解與認識R軟體的應用，進一步提高資料統計分析

Across the social sciences there has been increasing focus on reproducibility, i.e., the ability to examine a study's data and methods to ensure accuracy by reproducing the study. Reproducible Econome

Data-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before contin

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Py

This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data anal

This book offers new transparent views and step-by-step methods for performance evaluation of a set of units using Data Envelopment Analysis (DEA). The book has twelve practical chapters. Elementary c

Seismic data must be interpreted using digital signal processing techniques in order to create accurate representations of petroleum reservoirs and the interior structure of the Earth. This book provides an advanced overview of digital signal processing (DSP) and its applications to exploration seismology using real-world examples. The book begins by introducing seismic theory, describing how to identify seismic events in terms of signals and noise, and how to convert seismic data into the language of DSP. Deterministic DSP is then covered, together with non-conventional sampling techniques. The final part covers statistical seismic signal processing via Wiener optimum filtering, deconvolution, linear-prediction filtering and seismic wavelet processing. With over sixty end-of-chapter exercises, seismic data sets and data processing MATLAB codes included, this is an ideal resource for electrical engineering students unfamiliar with seismic data, and for Earth Scientists and petroleum pr

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic

Engaging and accessible, this book offers students a complete guide to using NVivo for qualitative data analysis. Drawing on their wealth of expertise, the authors offer detailed, practical advice tha

Engaging and accessible, this book offers students a complete guide to using NVivo for qualitative data analysis. Drawing on their wealth of expertise, the authors offer detailed, practical advice tha