Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track
Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track
Annotation To help you answer big data questions, this unique guide shows you how to use simple, fun, and elegant tools leveraging Apache Hadoop. You'll learn how to break problems into efficient data transformations to meet most of your analysis needs.
Within a year of President George W. Bush announcing the end of major combat operations in Iraq in May 2003, dozens of attacks by insurgents had claimed hundreds of civilian and military lives. Through 2004 and 2005, accounts from returning veterans presaged an unfolding strategic debacle—potentially made worse by U.S. tactics being focused on extending conventionally oriented military operations rather than on adapting to the insurgency. By 2007, however, a sea change had taken place, and some U.S. units were integrating counterinsurgency tactics and full-spectrum operations to great effect. In the main, the government and the media cited three factors for having turned the tide on the battlefield: the promulgation of a new joint counterinsurgency doctrine, the "surge" in troop numbers, and the appointment of General David Petraeus as senior military commander. James Russell, however, contends that local security had already improved greatly in Anbar and Ninewah between 2005 and 2007 thanks to the innovative actions of brigade and company commanders—evidenced most notably in the turning of tribal leaders against Al Qaeda. In Innovation, Transformation, and War, he goes behind the headlines to reveal—through extensive field research and face-to-face interviews with military and civilian personnel of all ranks—how a group of Army and Marine Corps units successfully innovated in an unprecedented way: from the bottom up as well as from the top down. In the process they transformed themselves from organizations structured and trained for conventional military operations into ones with a unique array of capabilities for a full spectrum of combat operations. As well as telling an inspiring story, this book will be an invaluable reference for anyone tasked with driving innovation in any kind of complex organization.
Terra Incognita is the most comprehensive bibliography of sources related to the Great Smoky Mountains ever created. Compiled and edited by three librarians, this authoritative and meticulously researched work is an indispensable reference for scholars and students studying any aspect of the region’s past. Starting with the de Soto map of 1544, the earliest document that purports to describe anything about the Great Smoky Mountains, and continuing through 1934 with the establishment of the Great Smoky Mountains National Park—today the most visited national park in the United States—this volume catalogs books, periodical and journal articles, selected newspaper reports, government publications, dissertations, and theses published during that period. This bibliography treats the Great Smoky Mountain Region in western North Carolina and east Tennessee systematically and extensively in its full historic and social context. Prefatory material includes a timeline of the Great Smoky Mountains and a list of suggested readings on the era covered. The book is divided into thirteen thematic chapters, each featuring an introductory essay that discusses the nature and value of the materials in that section. Following each overview is an annotated bibliography that includes full citation information and a bibliographic description of each entry. Chapters cover the history of the area; the Cherokee in the Great Smoky Mountains; the national forest movement and the formation of the national park; life in the locality; Horace Kephart, perhaps the most important chronicler to document the mountains and their inhabitants; natural resources; early travel; music; literature; early exploration and science; maps; and recreation and tourism. Sure to become a standard resource on this rich and vital region, Terra Incognita is an essential acquisition for all academic and public libraries and a boundless resource for researchers and students of the region.
Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track
To help you answer big data questions, this unique guide shows you how to use simple, fun, and elegant tools leveraging Apache Hadoop. You'll learn how to break problems into efficient data transformations to meet most of your analysis needs. Its developer-friendly approach works well for anyone using Hadoop, and flattens the learning curve for those working with big data for the first time. Written by Philip Kromer, founder and CTO at Infochimps, this book uses real data and real problems to illustrate patterns found across knowledge domains. It equips you with a fundamental toolkit for performing statistical summaries, text mining, spatial and time-series analysis, and light machine learning. For those working in an elastic cloud environment, you'll learn superpowers that make exploratory analytics especially efficient. Learn from detailed example programs that apply Hadoop to interesting problems in context Gain advice and best practices for efficient software development Discover how to think at scale by understanding how data must flow through the cluster to effect transformations Identify the tuning knobs that matter, and rules-of-thumb to know when they're needed
Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track
Finding patterns in massive event streams can be difficult, but learning how to find them doesn’t have to be. This unique hands-on guide shows you how to solve this and many other problems in large-scale data processing with simple, fun, and elegant tools that leverage Apache Hadoop. You’ll gain a practical, actionable view of big data by working with real data and real problems. Perfect for beginners, this book’s approach will also appeal to experienced practitioners who want to brush up on their skills. Part I explains how Hadoop and MapReduce work, while Part II covers many analytic patterns you can use to process any data. As you work through several exercises, you’ll also learn how to use Apache Pig to process data. Learn the necessary mechanics of working with Hadoop, including how data and computation move around the cluster Dive into map/reduce mechanics and build your first map/reduce job in Python Understand how to run chains of map/reduce jobs in the form of Pig scripts Use a real-world dataset—baseball performance statistics—throughout the book Work with examples of several analytic patterns, and learn when and where you might use them
This bibliography lists the books, paintings, and portraits of the mystic Irish poet George William Russell, best known by his pseudonym, "AE." Russell was a late nineteenth-and early twentieth century Irish poet and essayist whose first book of poems, Homeward: Songs by the Way (1894), established him in what was known as the Irish Literary Revival.
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
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