Big Data Analytics with R

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Author : Simon Walkowiak
Genre : Computers

Summary : Utilize R to uncover hidden patterns in your Big Data Key Features [* ] Perform computational analyses on Big Data to generate meaningful results [*] Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases, [*] Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the market Book DescriptionBig Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O. What you will learn [*] Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities [*] Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner [*] Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usage [*] Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platform Who this book is for This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows. It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.

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Author : Pallavi Chavan
Genre : Computers

Summary : Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments. - Includes hands-on Tutorials and Case Studies: Structured exercises and real-world examples reinforce learning and skill-building - Focuses on Python and R for Big Data: Detailed lessons in Python and R programming cater to the increasing demand for data science expertise - Balanced Theory and Practice: Comprehensive coverage ensures a strong theoretical foundation paired with actionable insights for real-world application

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Author : Mothiram Rajasekaran
Genre : Computers

Summary : Mothiram Rajasekaran,Senior Solution Consultant, Cloudera, USA.

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Author : Vignesh Prajapati
Genre : Apache Hadoop

Summary : Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R.

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Author : Mark Hornick
Genre : Computers

Summary : Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. The Oracle Press Guide to Big Data Analytics using R Cowritten by members of the Big Data team at Oracle, this Oracle Press book focuses on analyzing data with R while making it scalable using Oracle’s R technologies. Using R to Unlock the Value of Big Data provides an introduction to open source R and describes issues with traditional R and database interaction. The book then offers in-depth coverage of Oracle’s strategic R offerings: Oracle R Enterprise, Oracle R Distribution, ROracle, and Oracle R Connector for Hadoop. You can practice your new skills using the end-of-chapter exercises.

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Author : Dr. A. Veneela
Genre : Antiques & Collectibles

Summary : The emergence of Quantum Computing and Data Analytics represents a transformative shift in the way complex computational problems are approached and solved in the modern digital era. As the volume, velocity, and variety of data continue to grow exponentially, conventional computing systems increasingly encounter limitations in processing highly complex datasets and optimization problems. Quantum computing, based on the principles of quantum mechanics, offers unprecedented computational capabilities that have the potential to revolutionize data analysis, machine learning, artificial intelligence, cryptography, scientific research, and decision-making systems. Simultaneously, the programming language R has established itself as one of the most powerful and widely adopted tools for statistical computing, data visualization, predictive modeling, and advanced analytics. The integration of quantum computing concepts with data analytics techniques using R programming creates exciting opportunities for researchers, students, and professionals seeking to explore the future of intelligent computation. The book "Quantum Computing and Data Analytics Using R Programming" has been developed with the objective of providing a comprehensive and systematic understanding of both quantum computing principles and modern data analytics methodologies. The rapid advancements in computational technologies have created a growing demand for educational resources that bridge theoretical concepts with practical implementation. This book aims to address that need by presenting foundational knowledge, analytical techniques, programming approaches, and emerging applications in a structured and accessible manner. The text begins by introducing the fundamental concepts of quantum mechanics that form the basis of quantum computing, including quantum bits, superposition, entanglement, quantum gates, and quantum circuits. These concepts are explained in a manner that enables readers from diverse academic backgrounds to understand the underlying principles without requiring extensive prior knowledge of quantum physics. Building upon these foundations, the book explores quantum algorithms, quantum computational models, and their potential applications in solving complex real-world problems. One of the distinguishing features of this book is its interdisciplinary approach. Rather than treating quantum computing and data analytics as separate subjects, the text highlights their growing convergence and complementary strengths. Emerging developments in quantum machine learning, quantum optimization, quantum-enhanced analytics, and intelligent data processing are discussed to provide readers with insights into future technological trends. The book also examines applications in healthcare, finance, cybersecurity, business intelligence, scientific research, engineering, and decision-support systems, illustrating the broad impact of these technologies on contemporary society. We express our sincere gratitude to our institutions, colleagues, students, research collaborators, and the broader scientific community whose support and inspiration have contributed significantly to the completion of this work. We are particularly thankful to the researchers and pioneers whose groundbreaking contributions in quantum mechanics, computer science, statistics, machine learning, and data analytics have established the foundations upon which this book is built. It is our earnest hope that "Quantum Computing and Data Analytics Using R Programming" will serve as a valuable resource for undergraduate students, postgraduate learners, research scholars, faculty members, data scientists, software professionals, and industry practitioners. We trust that this book will inspire readers to explore innovative computational paradigms, engage in interdisciplinary research, and contribute to the advancement of knowledge in the rapidly evolving fields of quantum technologies and data analytics. As technology continues to reshape the future of science, industry, and society, the integration of quantum computing and advanced data analytics will play an increasingly important role in addressing complex global challenges and unlocking new opportunities for innovation. We hope this book serves as a meaningful step in that exciting journey.

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Author : Umesh R Hodeghatta
Genre : Computers

Summary : Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictiveanalytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.