Big Data Analytics Methods

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Big Data Analytics Methods

Peter Ghavami
Big Data Analytics Methods Book PDF

Author : Peter Ghavami
Publisher : Walter de Gruyter GmbH & Co KG
Page : 282 pages
File Size : 50,8 Mb
Release : 2019-12-16
Category : Business & Economics
ISBN : 9781547401581

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Big Data Analytics Methods by Peter Ghavami Pdf

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

Big Data Analytics

Saumyadipta Pyne,B.L.S. Prakasa Rao,S.B. Rao
Big Data Analytics Book PDF

Author : Saumyadipta Pyne,B.L.S. Prakasa Rao,S.B. Rao
Publisher : Springer
Page : 276 pages
File Size : 52,6 Mb
Release : 2016-10-12
Category : Computers
ISBN : 9788132236283

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Big Data Analytics by Saumyadipta Pyne,B.L.S. Prakasa Rao,S.B. Rao Pdf

This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.

Big Data Analytics Methods

Peter Ghavami
Big Data Analytics Methods Book PDF

Author : Peter Ghavami
Publisher : Walter de Gruyter GmbH & Co KG
Page : 282 pages
File Size : 52,6 Mb
Release : 2019-12-16
Category : Business & Economics
ISBN : 9781547401581

Get Book

Big Data Analytics Methods by Peter Ghavami Pdf

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

Big Data Analytics Methods

Peter Ghavami
Big Data Analytics Methods Book PDF

Author : Peter Ghavami
Publisher : Createspace Independent Publishing Platform
Page : 304 pages
File Size : 49,7 Mb
Release : 2016-03-06
Category : Electronic
ISBN : 1530414830

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Big Data Analytics Methods by Peter Ghavami Pdf

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensemble of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods are covered. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. This book is ideal as a text book for a course or as a reference for data scientists, data engineers, data analysts, Business intelligence practitioners, and business managers. It covers 10 chapters that discuss natural language processing (NLP), data visualization, prediction, optimization, artificial intelligence, regression analysis, cox hazard model and many analytics use case examples with applications in healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services. Big Data Analytics Methods Is a must read for those who wish to gain confidence and knowledge about big data and advanced analytics techniques. Read this book and confidently speak, lead and guide others about machine learning, neural networks, NLP, deep learning, and over 100 other analytics techniques. This book is fun and easy to read. It starts with simple and broad explanation of methods and gradually introduces more technical terms and techniques layer by layer. It finally introduces the underlying mathematical terms for those who want a mathematical foundation of the analytics methods. This book is one of a kind as it provides state of the art in advanced data analytics methods with important best practices to ensure the reader's success in data analytics.

Data Analytics and Big Data

Soraya Sedkaoui
Data Analytics and Big Data Book PDF

Author : Soraya Sedkaoui
Publisher : John Wiley & Sons
Page : 224 pages
File Size : 55,8 Mb
Release : 2018-05-24
Category : Computers
ISBN : 9781119528050

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Data Analytics and Big Data by Soraya Sedkaoui Pdf

The main purpose of this book is to investigate, explore and describe approaches and methods to facilitate data understanding through analytics solutions based on its principles, concepts and applications. But analyzing data is also about involving the use of software. For this, and in order to cover some aspect of data analytics, this book uses software (Excel, SPSS, Python, etc) which can help readers to better understand the analytics process in simple terms and supporting useful methods in its application.

Predictive Analytics, Data Mining and Big Data

S. Finlay
Predictive Analytics Data Mining and Big Data Book PDF

Author : S. Finlay
Publisher : Springer
Page : 241 pages
File Size : 40,7 Mb
Release : 2014-07-01
Category : Business & Economics
ISBN : 9781137379283

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Predictive Analytics, Data Mining and Big Data by S. Finlay Pdf

This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.

Applications of Big Data Analytics

Mohammed M. Alani,Hissam Tawfik,Mohammed Saeed,Obinna Anya
Applications of Big Data Analytics Book PDF

Author : Mohammed M. Alani,Hissam Tawfik,Mohammed Saeed,Obinna Anya
Publisher : Springer
Page : 214 pages
File Size : 45,6 Mb
Release : 2018-07-23
Category : Computers
ISBN : 9783319764726

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Applications of Big Data Analytics by Mohammed M. Alani,Hissam Tawfik,Mohammed Saeed,Obinna Anya Pdf

This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.

Data Science and Big Data Analytics

EMC Education Services
Data Science and Big Data Analytics Book PDF

Author : EMC Education Services
Publisher : John Wiley & Sons
Page : 432 pages
File Size : 49,8 Mb
Release : 2015-01-05
Category : Computers
ISBN : 9781118876053

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Data Science and Big Data Analytics by EMC Education Services Pdf

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Clustering Methods for Big Data Analytics

Olfa Nasraoui,Chiheb-Eddine Ben N'Cir
Clustering Methods for Big Data Analytics Book PDF

Author : Olfa Nasraoui,Chiheb-Eddine Ben N'Cir
Publisher : Springer
Page : 187 pages
File Size : 45,7 Mb
Release : 2018-10-27
Category : Technology & Engineering
ISBN : 9783319978642

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Clustering Methods for Big Data Analytics by Olfa Nasraoui,Chiheb-Eddine Ben N'Cir Pdf

This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.

Data Science in Theory and Practice

Maria Cristina Mariani,Osei Kofi Tweneboah,Maria Pia Beccar-Varela
Data Science in Theory and Practice Book PDF

Author : Maria Cristina Mariani,Osei Kofi Tweneboah,Maria Pia Beccar-Varela
Publisher : John Wiley & Sons
Page : 404 pages
File Size : 43,6 Mb
Release : 2021-10-12
Category : Mathematics
ISBN : 9781119674689

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Data Science in Theory and Practice by Maria Cristina Mariani,Osei Kofi Tweneboah,Maria Pia Beccar-Varela Pdf

DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

Data Science and Big Data Analytics in Smart Environments

Marta Chinnici,Florin Pop,Catalin Negru
Data Science and Big Data Analytics in Smart Environments Book PDF

Author : Marta Chinnici,Florin Pop,Catalin Negru
Publisher : CRC Press
Page : 305 pages
File Size : 42,6 Mb
Release : 2021-07-28
Category : Computers
ISBN : 9781000386011

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Data Science and Big Data Analytics in Smart Environments by Marta Chinnici,Florin Pop,Catalin Negru Pdf

Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment. Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.

Big Data

Kuan-Ching Li,Hai Jiang,Laurence T. Yang,Alfredo Cuzzocrea
Big Data Book PDF

Author : Kuan-Ching Li,Hai Jiang,Laurence T. Yang,Alfredo Cuzzocrea
Publisher : CRC Press
Page : 498 pages
File Size : 54,5 Mb
Release : 2015-02-23
Category : Computers
ISBN : 9781482240566

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Big Data by Kuan-Ching Li,Hai Jiang,Laurence T. Yang,Alfredo Cuzzocrea Pdf

As today's organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages.Pre

Handbook of Big Data Analytics

Vadlamani Ravi,Aswani Kumar Cherukuri
Handbook of Big Data Analytics Book PDF

Author : Vadlamani Ravi,Aswani Kumar Cherukuri
Publisher : IET
Page : 390 pages
File Size : 47,5 Mb
Release : 2021-07-09
Category : Computers
ISBN : 9781839530647

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Handbook of Big Data Analytics by Vadlamani Ravi,Aswani Kumar Cherukuri Pdf

This comprehensive edited 2-volume handbook provides a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics. The first volume presents methodologies that support Big Data analytics, while the second volume offers a wide range of Big Data analytics applications.

Big Data for Insurance Companies

Marine Corlosquet-Habart,Jacques Janssen
Big Data for Insurance Companies Book PDF

Author : Marine Corlosquet-Habart,Jacques Janssen
Publisher : John Wiley & Sons
Page : 190 pages
File Size : 46,6 Mb
Release : 2018-03-13
Category : Business & Economics
ISBN : 9781786300737

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Big Data for Insurance Companies by Marine Corlosquet-Habart,Jacques Janssen Pdf

This book will be a "must" for people who want good knowledge of big data concepts and their applications in the real world, particularly in the field of insurance. It will be useful to people working in finance and to masters students using big data tools. The authors present the bases of big data: data analysis methods, learning processes, application to insurance and position within the insurance market. Individual chapters a will be written by well-known authors in this field.

Practical Big Data Analytics

Nataraj Dasgupta
Practical Big Data Analytics Book PDF

Author : Nataraj Dasgupta
Publisher : Packt Publishing Ltd
Page : 402 pages
File Size : 54,5 Mb
Release : 2018-01-15
Category : Computers
ISBN : 9781783554409

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Practical Big Data Analytics by Nataraj Dasgupta Pdf

Get command of your organizational Big Data using the power of data science and analytics Key Features A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data Book Description Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book. What you will learn - Get a 360-degree view into the world of Big Data, data science and machine learning - Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives - Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R - Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions - Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications - Understand corporate strategies for successful Big Data and data science projects - Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies Who this book is for The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.