Big data (Plus de 44 000 résultats)

Paperback. Etat : Good. ?This text should be required reading for everyone in contemporary business.? --Peter Woodhull, CEO, Modus21 ?The one book that clearly describes and links Big Data concepts to business utility.? --Dr. Christopher Starr, PhD ?Simply, this is the best Big Data book on the market!? --Sam Rostam, Cascadian IT Group .one of the most contemporary approaches I?ve seen to Big Data fundamentals.? --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data?s fundamental concepts and what makes it different from previous forms of data analysis and data scienceUnderstanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovationPlanning strategic, business-driven Big Data initiativesAddressing considerations such as data management, governance, and securityRecognizing the 5 ?V? characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and valueClarifying Big Data?s relationships with OLTP, OLAP, ETL, data warehouses, and data martsWorking with Big Data in structured, unstructured, semi-structured, and metadata formatsIncreasing value by integrating Big Data resources with corporate performance monitoringUnderstanding how Big Data leverages distributed and parallel processingUsing NoSQL and other technologies to meet Big Data?s distinct data processing requirementsLeveraging statistical approaches of quantitative and qualitative analysisApplying computational analysis methods, including machine learning.…

Paperback. Etat : Very Good. ?This text should be required reading for everyone in contemporary business.? --Peter Woodhull, CEO, Modus21 ?The one book that clearly describes and links Big Data concepts to business utility.? --Dr. Christopher Starr, PhD ?Simply, this is the best Big Data book on the market!? --Sam Rostam, Cascadian IT Group .one of the most contemporary approaches I?ve seen to Big Data fundamentals.? --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data?s fundamental concepts and what makes it different from previous forms of data analysis and data scienceUnderstanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovationPlanning strategic, business-driven Big Data initiativesAddressing considerations such as data management, governance, and securityRecognizing the 5 ?V? characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and valueClarifying Big Data?s relationships with OLTP, OLAP, ETL, data warehouses, and data martsWorking with Big Data in structured, unstructured, semi-structured, and metadata formatsIncreasing value by integrating Big Data resources with corporate performance monitoringUnderstanding how Big Data leverages distributed and parallel processingUsing NoSQL and other technologies to meet Big Data?s distinct data processing requirementsLeveraging statistical approaches of quantitative and qualitative analysisApplying computational analysis methods, including machine learning.…

Paperback. Etat : Very Good. Be part of the big learning data revolution.

Paperback. Etat : Good.

Paperback. Etat : Very Good.

Paperback. Etat : Very Good.

Hardback. Etat : Good. Explore why ? now more than ever ? the world is in a race to become data-driven, and how you can learn from examples of data-driven leadership in an Age of Disruption, Big Data, and AI In Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI, Fortune 1000 strategic advisor, noted author, and distinguished thought leader Randy Bean tells the story of the rise of Big Data and its business impact ? its disruptive power, the cultural challenges to becoming data-driven, the importance of data ethics, and the future of data-driven AI. The book looks at the impact of Big Data during a period of explosive information growth, technology advancement, emergence of the Internet and social media, and challenges to accepted notions of data, science, and facts, and asks what it means to become "data-driven." Fail Fast, Learn Faster includes discussions of: The emergence of Big Data and why organizations must become data-driven to surviveWhy becoming data-driven forces companies to "think different" about their businessThe state of data in the corporate world today, and the principal challengesWhy companies must develop a true "data culture" if they expect to changeExamples of companies that are demonstrating data-driven leadership and what we can learn from themWhy companies must learn to "fail fast and learn faster" to compete in the years aheadHow the Chief Data Officer has been established as a new corporate profession Written for CEOs and Corporate Board Directors, data professional and practitioners at all organizational levels, university executive programs and students entering the data profession, and general readers seeking to understand the Information Age and why data, science, and facts matter in the world in which we live, Fail Fast, Learn Faster p;is essential reading that delivers an urgent message for the business leaders of today and of the future.…

Paperback. Etat : Very Good. Learn how to profit from an effective data strategy in a world of big data, analytics and the Internet of Things.

Paperback. Etat : Good. Learn how to profit from an effective data strategy in a world of big data, analytics and the Internet of Things.

Hardback. Etat : Very Good. The new secret to driving Loyalty that pays. Once revolutionary, loyalty programs designed to differentiate products quickly became commoditized. This book shows you how to create a system powered by human motivation and digital technology that creates ongoing, persistent engagement among customers, employees, and partners.…

Hardback. Etat : Very Good. The essential playbook for the future of your business What To Do When Machines Do Everything is a guidebook to succeeding in the next generation of the digital economy.

Hardback. Etat : Good. The essential playbook for the future of your business What To Do When Machines Do Everything is a guidebook to succeeding in the next generation of the digital economy.

paperback. Etat : Very Good.

paperback. Etat : Fair.

paperback. Etat : As New.

Paperback. Etat : Very Good.

Paperback. Etat : Very Good. Data in all domains is getting bigger. How can you work with it efficiently? This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark's powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables.…

Paperback. Etat : Good. Data in all domains is getting bigger. How can you work with it efficiently? This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark's powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables.…

Hardback. Etat : Good. A FINANCIAL TIMES BOOK OF THE MONTH FROM THE WALL STREET JOURNAL: "Nothing Mr. Gilder says or writes is ever delivered at anything less than the fullest philosophical decibel. Mr. Gilder sounds less like a tech guru than a poet, and his words tumble out in a romantic cascade." ?Google?s algorithms assume the world?s future is nothing more than the next moment in a random process. George Gilder shows how deep this assumption goes, what motivates people to make it, and why it?s wrong: the future depends on human action.? ? Peter Thiel, founder of PayPal and Palantir Technologies and author of Zero to One: Notes on Startups, or How to Build the Future The Age of Google, built on big data and machine intelligence, has been an awesome era. But it?s coming to an end. In Life after Google, George Gilder?the peerless visionary of technology and culture?explains why Silicon Valley is suffering a nervous breakdown and what to expect as the post-Google age dawns. Google?s astonishing ability to ?search and sort? attracts the entire world to its search engine and countless other goodies?videos, maps, email, calendars.And everything it offers is free, or so it seems. Instead of paying directly, users submit to advertising. The system of ?aggregate and advertise? works?for a while?if you control an empire of data centers, but a market without prices strangles entrepreneurship and turns the Internet into a wasteland of ads. The crisis is not just economic. Even as advances in artificial intelligence induce delusions of omnipotence and transcendence, Silicon Valley has pretty much given up on security. The Internet firewalls supposedly protecting all those passwords and personal information have proved hopelessly permeable. The crisis cannot be solved within the current computer and network architecture. The future lies with the ?cryptocosm??the new architecture of the blockchain and its derivatives. Enabling cryptocurrencies such as bitcoin and ether, NEO and Hashgraph, it will provide the Internet a secure global payments system, ending the aggregate-and-advertise Age of Google. Silicon Valley, long dominated by a few giants, faces a ?great unbundling,? which will disperse computer power and commerce and transform the economy and the Internet. Life after Google is almost here.   For fans of "Wealth and Poverty," "Knowledge and Power," and "The Scandal of Money.". …

Paperback. Etat : Good. Leverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets.

paperback. Etat : Fair.

Paperback. Etat : Fair.

Hardcover. Etat : As New.

Hardcover. Etat : Very Good.

Hardcover. Etat : Very Good.

Paperback. Etat : Good. NEW YORK TIMES BESTSELLER * A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric--with a new afterword   "A manual for the twenty-first-century citizen . . . relevant and urgent."--Financial Times   NATIONAL BOOK AWARD LONGLIST * NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review * The Boston Globe * Wired * Fortune * Kirkus Reviews * The Guardian * Nature * On Point   We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we can get a job or a loan, how much we pay for health insurance--are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.   But as mathematician and data scientist Cathy O'Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination--propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data.…

Paperback. Etat : Very Good. NEW YORK TIMES BESTSELLER * A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric--with a new afterword   "A manual for the twenty-first-century citizen . . . relevant and urgent."--Financial Times   NATIONAL BOOK AWARD LONGLIST * NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review * The Boston Globe * Wired * Fortune * Kirkus Reviews * The Guardian * Nature * On Point   We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we can get a job or a loan, how much we pay for health insurance--are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.   But as mathematician and data scientist Cathy O'Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination--propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data.…

Paperback. Etat : Good. Offers a practical state-of-the-field overview of big data as both a means and an object of research, with essays from prominent and emerging scholars. Part 1 explores how the advent of geoweb technologies and big data sets has influenced some of geography's major subdisciplines. Part 2 addresses how the geographic study of big data has implications for other disciplinary fields.…

hardcover. Etat : Fair.

Paperback. Etat : Good. When data from all aspects of our lives can be relevant to our health - from our habits at the grocery store and our Google searches to our FitBit data and our medical records - can we really differentiate between big data and health big data? Will health big data be used for good, such as to improve drug safety, or ill, as in insurance discrimination? Will it disrupt health care (and the health care system) as we know it? Will it be possible to protect our health privacy? What barriers will there be to collecting and utilizing health big data? What role should law play, and what ethical concerns may arise? This timely, groundbreaking volume explores these questions and more from a variety of perspectives, examining how law promotes or discourages the use of big data in the health care sphere, and also what we can learn from other sectors.…