Download Full eBooks in PDF
Open Source Software For Statistical Analysis Of Big Data Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Open Source Software For Statistical Analysis Of Big Data book. This book definitely worth reading, it is an incredibly well-written.
Author : Richard Segall,Gao Niu
Publisher : Engineering Science Reference
Page : 128 pages
File Size : 43,5 Mb
Release : 2020
Category : Big data
ISBN : 1799827690
"This book explores topics in the field of open source software for big data"--
Author : Segall, Richard S.,Niu, Gao
Publisher : IGI Global
Page : 237 pages
File Size : 47,6 Mb
Release : 2020-02-21
Category : Computers
ISBN : 9781799827702
With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.
Author : Management Association, Information Resources
Publisher : IGI Global
Page : 904 pages
File Size : 47,5 Mb
Release : 2021-06-25
Category : Computers
ISBN : 9781799891598
The quick growth of computer technology and development of software caused it to be in a constant state of change and advancement. This advancement in software development meant that there would be many types of software developed in order to excel in usability and efficiency. Among these different types of software was open source software, one that grants permission for users to use, study, change, and distribute it freely. Due to its availability, open source software has quickly become a valuable asset to the world of computer technology and across various disciplines including education, business, and library science. The Research Anthology on Usage and Development of Open Source Software presents comprehensive research on the design and development of open source software as well as the ways in which it is used. The text discusses in depth the way in which this computer software has been made into a collaborative effort for the advancement of software technology. Discussing topics such as ISO standards, big data, fault prediction, open collaboration, and software development, this anthology is essential for computer engineers, software developers, IT specialists and consultants, instructors, librarians, managers, executives, professionals, academicians, researchers, and students.
Author : Sun, Zhaohao
Publisher : IGI Global
Page : 335 pages
File Size : 55,9 Mb
Release : 2019-02-22
Category : Computers
ISBN : 9781522572787
Big data, analytics, and artificial intelligence are revolutionizing work, management, and lifestyles and are becoming disruptive technologies for healthcare, e-commerce, and web services. However, many fundamental, technological, and managerial issues for developing and applying intelligent big data analytics in these fields have yet to be addressed. Managerial Perspectives on Intelligent Big Data Analytics is a collection of innovative research that discusses the integration and application of artificial intelligence, business intelligence, digital transformation, and intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine learning, and fuzzy logic, this book is ideally designed for students, government officials, data scientists, managers, consultants, analysts, IT specialists, academicians, researchers, and industry professionals in fields that include big data, artificial intelligence, computing, and commerce.
Author : Constandinos X. Mavromoustakis,George Mastorakis,Ciprian Dobre
Publisher : Springer
Page : 382 pages
File Size : 49,9 Mb
Release : 2016-11-19
Category : Technology & Engineering
ISBN : 9783319451459
This book reports on the latest advances on the theories, practices, standards and strategies that are related to the modern technology paradigms, the Mobile Cloud computing (MCC) and Big Data, as the pillars and their association with the emerging 5G mobile networks. The book includes 15 rigorously refereed chapters written by leading international researchers, providing the readers with technical and scientific information about various aspects of Big Data and Mobile Cloud Computing, from basic concepts to advanced findings, reporting the state-of-the-art on Big Data management. It demonstrates and discusses methods and practices to improve multi-source Big Data manipulation techniques, as well as the integration of resources availability through the 3As (Anywhere, Anything, Anytime) paradigm, using the 5G access technologies.
Author : Philipp K. Janert
Publisher : "O'Reilly Media, Inc."
Page : 540 pages
File Size : 53,9 Mb
Release : 2010-11-11
Category : Computers
ISBN : 1449396658
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora
Author : Christopher P Halter
Publisher : Unknown
Page : 308 pages
File Size : 40,9 Mb
Release : 2020-01-14
Category : Electronic
ISBN : 1660228182
This introduction to quantitative analysis is designed is to assist the novice social science and educational researcher in interpreting statistical output data using the JASP open-source statistical analysis software. Through the examples and guidance, you will be able to select the statistical test that is appropriate for your data, apply the inferential test to your data, and interpret a statistical test's output table. These analysis methods include Contingency Tables, t-Tests, ANOVA, ANCOVA, Correlation, Linear Regression, Binomial tests, and Binomial Logistic Regression. The guide also includes procedures for Reliability and Exploratory Factor Analysis (EFA). The JASP application supports both Frequentist and Bayesian procedures. This guide is an update from Exploring Statistical Analysis (2018).
Author : Jason Williamson
Publisher : John Wiley & Sons
Page : 264 pages
File Size : 40,6 Mb
Release : 2014-12-31
Category : Computers
ISBN : 9781118903407
Hone your analytic talents and become part of the next big thing Getting a Big Data Job For Dummies is the ultimate guide to landing a position in one of the fastest-growing fields in the modern economy. Learn exactly what "big data" means, why it's so important across all industries, and how you can obtain one of the most sought-after skill sets of the decade. This book walks you through the process of identifying your ideal big data job, shaping the perfect resume, and nailing the interview, all in one easy-to-read guide. Companies from all industries, including finance, technology, medicine, and defense, are harnessing massive amounts of data to reap a competitive advantage. The demand for big data professionals is growing every year, and experts forecast an estimated 1.9 million additional U.S. jobs in big data by 2015. Whether your niche is developing the technology, handling the data, or analyzing the results, turning your attention to a career in big data can lead to a more secure, more lucrative career path. Getting a Big Data Job For Dummies provides an overview of the big data career arc, and then shows you how to get your foot in the door with topics like: The education you need to succeed The range of big data career path options An overview of major big data employers A plan to develop your job-landing strategy Your analytic inclinations may be your ticket to long-lasting success. In a highly competitive job market, developing your data skills can create a situation where you pick your employer rather than the other way around. If you're ready to get in on the ground floor of the next big thing, Getting a Big Data Job For Dummies will teach you everything you need to know to get started today.
Author : Bin Liang,Shu-Hong Gao,Hongcheng Wang
Publisher : Elsevier
Page : 671 pages
File Size : 50,6 Mb
Release : 2024-06-28
Category : Science
ISBN : 9780443141713
Water Security: Big Data-Driven Risk Identification, Assessment and Control of Emerging Contaminants contains the latest information on big data–driven risk detection and analysis, risk assessment and environmental health effect, intelligent risk control technologies, and global control strategy of emerging contaminants. First, this book highlights advances and challenges throughout the detection of emerging chemical contaminants (e.g., antimicrobials, microplastics) by sensors or mass spectrometry, as well as emerging biological contaminant (e.g., ARGs, pathogens) by a combination of next- and third-generation sequencing technologies in aquatic environment. Second, it discusses in depth the ecological risk assessment and environmental health effects of emerging contaminants. Lastly, it presents the most up-to-date intelligent risk management technologies. This book shares instrumental global strategy and policy analysis on how to control emerging contaminants. Offering interdisciplinary and global perspectives from experts in environmental sciences and engineering, environmental microbiology and microbiome, environmental informatics and bioinformatics, intelligent systems, and knowledge engineering, this book provides an accessible and flexible resource for researchers and upper level students working in these fields. Covers the detection, high-throughput analyses, and environmental behavior of the typical emerging chemical and biological contaminants Focuses on chemical and biological big data driven aquatic ecological risk assessment models and techniques Highlights the intelligent management and control technologies and policies for emerging contaminants in water environments
Author : Segall, Richard S.,Niu, Gao
Publisher : IGI Global
Page : 394 pages
File Size : 51,6 Mb
Release : 2022-01-07
Category : Computers
ISBN : 9781799884576
During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines.
Author : Venkat Ankam
Publisher : Packt Publishing Ltd
Page : 326 pages
File Size : 47,5 Mb
Release : 2016-09-28
Category : Computers
ISBN : 9781785889707
A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science
Author : Prashant Natarajan,John C. Frenzel,Detlev H. Smaltz
Publisher : CRC Press
Page : 210 pages
File Size : 48,8 Mb
Release : 2017-02-15
Category : Medical
ISBN : 9781315389318
Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.
Author : Simone Gressel,David J. Pauleen,Nazim Taskin
Publisher : SAGE
Page : 354 pages
File Size : 52,9 Mb
Release : 2020-10-12
Category : Business & Economics
ISBN : 9781529738285
Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.
Author : Singh, Manoj Kumar,G., Dileep Kumar
Publisher : IGI Global
Page : 324 pages
File Size : 52,8 Mb
Release : 2016-06-20
Category : Computers
ISBN : 9781522501831
“Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data.
Author : Prodromou, Theodosia
Publisher : IGI Global
Page : 365 pages
File Size : 40,7 Mb
Release : 2017-03-20
Category : Computers
ISBN : 9781522525134
Data visualization has emerged as a serious scholarly topic, and a wide range of tools have recently been developed at an accelerated pace to aid in this research area. Examining different ways of analyzing big data can result in increased efficiency for many corporations and organizations. Data Visualization and Statistical Literacy for Open and Big Data highlights methodological developments in the way that data analytics is both learned and taught. Featuring extensive coverage on emerging relevant topics such as data complexity, statistics education, and curriculum development, this publication is geared toward teachers, academicians, students, engineers, professionals, and researchers that are interested in expanding their knowledge of data examination and analysis.