Principles of Big Data

Principles of Big Data
Author: Jules J. Berman
Publsiher: Newnes
Total Pages: 288
Release: 2013-05-20
ISBN 10: 0124047246
ISBN 13: 9780124047242
Language: EN, FR, DE, ES & NL

Principles of Big Data Book Review:

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. Learn general methods for specifying Big Data in a way that is understandable to humans and to computers Avoid the pitfalls in Big Data design and analysis Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources

Big Data

Big Data
Author: Rajkumar Buyya,Rodrigo N. Calheiros,Amir Vahid Dastjerdi
Publsiher: Morgan Kaufmann
Total Pages: 494
Release: 2016-06-07
ISBN 10: 0128093463
ISBN 13: 9780128093467
Language: EN, FR, DE, ES & NL

Big Data Book Review:

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues. Covers computational platforms supporting Big Data applications Addresses key principles underlying Big Data computing Examines key developments supporting next generation Big Data platforms Explores the challenges in Big Data computing and ways to overcome them Contains expert contributors from both academia and industry

Big Data Management

Big Data Management
Author: Peter Ghavami
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 174
Release: 2020-11-09
ISBN 10: 3110664321
ISBN 13: 9783110664324
Language: EN, FR, DE, ES & NL

Big Data Management Book Review:

Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.

Big Data

Big Data
Author: Nathan Marz,James Warren
Publsiher: Manning Publications Company
Total Pages: 328
Release: 2015
ISBN 10: 9781617290343
ISBN 13: 1617290343
Language: EN, FR, DE, ES & NL

Big Data Book Review:

Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth

Applied Data Analytics Principles and Applications

Applied Data Analytics   Principles and Applications
Author: Johnson I. Agbinya
Publsiher: River Publishers Signal, Image
Total Pages: 300
Release: 2019-07-30
ISBN 10: 9788770220965
ISBN 13: 8770220964
Language: EN, FR, DE, ES & NL

Applied Data Analytics Principles and Applications Book Review:

The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very large data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors. Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualization systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts. This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.

Principles of Database Management

Principles of Database Management
Author: Wilfried Lemahieu,Seppe vanden Broucke,Bart Baesens
Publsiher: Cambridge University Press
Total Pages: 903
Release: 2018-07-12
ISBN 10: 1107186129
ISBN 13: 9781107186125
Language: EN, FR, DE, ES & NL

Principles of Database Management Book Review:

Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.

Principles and Methods for Data Science

Principles and Methods for Data Science
Author: Anonim
Publsiher: Elsevier
Total Pages: 496
Release: 2020-05-28
ISBN 10: 0444642129
ISBN 13: 9780444642127
Language: EN, FR, DE, ES & NL

Principles and Methods for Data Science Book Review:

Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Principles and Methods for Data Science

Information Governance Principles and Practices for a Big Data Landscape

Information Governance Principles and Practices for a Big Data Landscape
Author: Chuck Ballard,Cindy Compert,Tom Jesionowski,Ivan Milman,Bill Plants,Barry Rosen,Harald Smith,IBM Redbooks
Publsiher: IBM Redbooks
Total Pages: 280
Release: 2014-03-31
ISBN 10: 0738439592
ISBN 13: 9780738439594
Language: EN, FR, DE, ES & NL

Information Governance Principles and Practices for a Big Data Landscape Book Review:

This IBM® Redbooks® publication describes how the IBM Big Data Platform provides the integrated capabilities that are required for the adoption of Information Governance in the big data landscape. As organizations embark on new use cases, such as Big Data Exploration, an enhanced 360 view of customers, or Data Warehouse modernization, and absorb ever growing volumes and variety of data with accelerating velocity, the principles and practices of Information Governance become ever more critical to ensure trust in data and help organizations overcome the inherent risks and achieve the wanted value. The introduction of big data changes the information landscape. Data arrives faster than humans can react to it, and issues can quickly escalate into significant events. The variety of data now poses new privacy and security risks. The high volume of information in all places makes it harder to find where these issues, risks, and even useful information to drive new value and revenue are. Information Governance provides an organization with a framework that can align their wanted outcomes with their strategic management principles, the people who can implement those principles, and the architecture and platform that are needed to support the big data use cases. The IBM Big Data Platform, coupled with a framework for Information Governance, provides an approach to build, manage, and gain significant value from the big data landscape.

Principles of Managerial Statistics and Data Science

Principles of Managerial Statistics and Data Science
Author: Roberto Rivera
Publsiher: John Wiley & Sons
Total Pages: 688
Release: 2020-02-19
ISBN 10: 1119486416
ISBN 13: 9781119486411
Language: EN, FR, DE, ES & NL

Principles of Managerial Statistics and Data Science Book Review:

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

Ethics of Big Data

Ethics of Big Data
Author: Kord Davis
Publsiher: "O'Reilly Media, Inc."
Total Pages: 82
Release: 2012-09-13
ISBN 10: 1449357490
ISBN 13: 9781449357498
Language: EN, FR, DE, ES & NL

Ethics of Big Data Book Review:

What are your organization’s policies for generating and using huge datasets full of personal information? This book examines ethical questions raised by the big data phenomenon, and explains why enterprises need to reconsider business decisions concerning privacy and identity. Authors Kord Davis and Doug Patterson provide methods and techniques to help your business engage in a transparent and productive ethical inquiry into your current data practices. Both individuals and organizations have legitimate interests in understanding how data is handled. Your use of data can directly affect brand quality and revenue—as Target, Apple, Netflix, and dozens of other companies have discovered. With this book, you’ll learn how to align your actions with explicit company values and preserve the trust of customers, partners, and stakeholders. Review your data-handling practices and examine whether they reflect core organizational values Express coherent and consistent positions on your organization’s use of big data Define tactical plans to close gaps between values and practices—and discover how to maintain alignment as conditions change over time Maintain a balance between the benefits of innovation and the risks of unintended consequences

Big Data Analytics for Cloud IoT and Cognitive Computing

Big Data Analytics for Cloud  IoT and Cognitive Computing
Author: Kai Hwang,Min Chen
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2017-08-14
ISBN 10: 1119247020
ISBN 13: 9781119247029
Language: EN, FR, DE, ES & NL

Big Data Analytics for Cloud IoT and Cognitive Computing Book Review:

The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.

Principles of Data Science

Principles of Data Science
Author: Sinan Ozdemir
Publsiher: Packt Publishing Ltd
Total Pages: 388
Release: 2016-12-16
ISBN 10: 1785888927
ISBN 13: 9781785888922
Language: EN, FR, DE, ES & NL

Principles of Data Science Book Review:

Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.

Data Privacy

Data Privacy
Author: Nataraj Venkataramanan,Ashwin Shriram
Publsiher: CRC Press
Total Pages: 212
Release: 2016-10-03
ISBN 10: 1498721052
ISBN 13: 9781498721059
Language: EN, FR, DE, ES & NL

Data Privacy Book Review:

The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.

Big Data Analysis New Algorithms for a New Society

Big Data Analysis  New Algorithms for a New Society
Author: Nathalie Japkowicz,Jerzy Stefanowski
Publsiher: Springer
Total Pages: 329
Release: 2015-12-16
ISBN 10: 3319269895
ISBN 13: 9783319269894
Language: EN, FR, DE, ES & NL

Big Data Analysis New Algorithms for a New Society Book Review:

This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.

The Politics and Policies of Big Data

The Politics and Policies of Big Data
Author: Ann Rudinow Sætnan,Ingrid Schneider,Nicola Green
Publsiher: Routledge
Total Pages: 358
Release: 2018-05-08
ISBN 10: 1351866540
ISBN 13: 9781351866545
Language: EN, FR, DE, ES & NL

The Politics and Policies of Big Data Book Review:

Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden well-informed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance. An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance.

Principles of Data Wrangling

Principles of Data Wrangling
Author: Tye Rattenbury,Joseph M. Hellerstein,Jeffrey Heer,Sean Kandel,Connor Carreras
Publsiher: "O'Reilly Media, Inc."
Total Pages: 94
Release: 2017-06-29
ISBN 10: 1491938870
ISBN 13: 9781491938874
Language: EN, FR, DE, ES & NL

Principles of Data Wrangling Book Review:

A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?" Wrangling data consumes roughly 50-80% of an analyst’s time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors—time, granularity, scope, and structure—that you need to consider as you begin to work with data. You’ll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today’s data-driven organizations. Appreciate the importance—and the satisfaction—of wrangling data the right way. Understand what kind of data is available Choose which data to use and at what level of detail Meaningfully combine multiple sources of data Decide how to distill the results to a size and shape that can drive downstream analysis

Applied Predictive Analytics

Applied Predictive Analytics
Author: Dean Abbott
Publsiher: John Wiley & Sons
Total Pages: 456
Release: 2014-03-31
ISBN 10: 111872769X
ISBN 13: 9781118727690
Language: EN, FR, DE, ES & NL

Applied Predictive Analytics Book Review:

Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

Big Data Computing

Big Data Computing
Author: Rajendra Akerkar
Publsiher: CRC Press
Total Pages: 564
Release: 2013-12-05
ISBN 10: 1466578386
ISBN 13: 9781466578388
Language: EN, FR, DE, ES & NL

Big Data Computing Book Review:

Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches. Comprised of five sections, the book focuses on: What Big Data is and why it is important Semantic technologies Tools and methods Business and economic perspectives Big Data applications across industries

Process Safety and Big Data

Process Safety and Big Data
Author: Sagit Valeev,Natalya Kondratyeva
Publsiher: Elsevier
Total Pages: 312
Release: 2021-03-01
ISBN 10: 0128220678
ISBN 13: 9780128220672
Language: EN, FR, DE, ES & NL

Process Safety and Big Data Book Review:

Process Safety and Big Data discusses the principles of process safety and advanced information technologies, explaining its application to the process industry and providing examples of applications in process safety control and decision support systems. The book helps address problems that researchers face as the result of increased process complexity. The book shows ways to tackle safety issues by implementing modern information technologies, such as big data analysis and artificial intelligence. It provides an integrated approach to modern information technologies used in the control and management of process safety in industry. In addition, the book considers indicators and criteria in effective safety decision, helps safety managers understand how to make effective safety decisions, addresses the issue of how big data provides support for (autonomous) improved data-driven decisions, and provides indicators and criteria for big data in process safety. Paves the way for the digital transformation of safety science and safety management Takes a system approach to advanced information technologies used in process safety Provides examples on how AI is applied in the contextualization of data streams that are monitored to improve safety performance Applies big data technologies to process safety Includes case studies at the end of the book

Data Science and Big Data Analytics

Data Science and Big Data Analytics
Author: EMC Education Services
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2015-01-05
ISBN 10: 1118876059
ISBN 13: 9781118876053
Language: EN, FR, DE, ES & NL

Data Science and Big Data Analytics Book Review:

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 Corresponding data sets are available from the book’s page at Wiley which you can find on the Wiley site by searching for the ISBN 9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!