Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale

Lieferzeit: Lieferbar innerhalb 14 Tagen

41,49 

ISBN: 0134024141
ISBN 13: 9780134024141
Autor: Mendelevitch, Ofer/Stella, Casey/Eadline, Douglas
Verlag: Pearson Verlag
Umfang: 256 S.
Erscheinungsdatum: 12.12.2016
Auflage: 1/2016
Gewicht: 363 g
Produktform: Kartoniert
Einband: Kartoniert

This book provides a unique perspective on applying data science with Hadoop by explaining what data science with Hadoop is all about, its practical business applications, and then diving deep into the details and providing a hands-on tutorial and showcase of various use-cases from the real world. The authors bring together all the practical knowledge students will need to do real, useful data science with Hadoop.

Artikelnummer: 7711200 Kategorie:

Beschreibung

The Complete Guide to Data Science with HadoopFor Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. LearnWhat data science is, how it has evolved, and how to plan a data science careerHow data volume, variety, and velocity shape data science use casesHadoop and its ecosystem, including HDFS, MapReduce, YARN, and SparkData importation with Hive and SparkData quality, preprocessing, preparation, and modelingVisualization: surfacing insights from huge data setsMachine learning: classification, regression, clustering, and anomaly detectionAlgorithms and Hadoop tools for predictive modelingCluster analysis and similarity functionsLarge-scale anomaly detectionNLP: applying data science to human language

Autorenporträt

Ofer Mendelevitch is Vice President of Data Science at Lendup, where he is responsible for Lendups machine learning and advanced analytics group. Prior to joining Lendup, Ofer was Director of Data Science at Hortonworks, where he was responsible for helping Hortonworks customers apply Data Science with Hadoop and Spark to big data across various industries including healthcare, finance, retail and others. Before Hortonworks, Ofer served as Entrepreneur in Residence at XSeed Capital, VP of Engineering at Nor1, and Director of Engineering at Yahoo!. Casey Stella is a Principal Software Engineer focusing on Data Science at Hortonworks, which provides an open source Hadoop distribution. Caseys primary responsibility is leading the analytics/data science team for the Apache Metron (Incubating) Project, an open source cybersecurity project. Prior to Hortonworks, Casey was an architect at Explorys, which was a medical informatics startup spun out of the Cleveland Clinic. In the more distant past, Casey served as a developer at Oracle, Research Geophysicist at ION Geophysical and as a poor graduate student in Mathematics at Texas A&M. Douglas Eadline, PhD, began his career as analytical chemist with an interest in computer methods. Starting with the first Beowulf how-to document, Doug has written hundreds of articles, white papers, and instructional documents covering many aspects of HPC and Hadoop computing. Prior to starting and editing the popular ClusterMonkey.net website in 2005, he served as editor¿in¿chief for ClusterWorld Magazine and was senior HPC editor for Linux Magazine. He has practical hands-on experience in many aspects of HPC and Apache Hadoop, including hardware and software design, benchmarking, storage, GPU, cloud computing, and parallel computing. Currently, he is a writer and consultant to the HPC/analytics industry and leader of the Limulus Personal Cluster Project (http://limulus.basement-supercomputing.com). He is author of the Apache Hadoop® Fundamentals LiveLessons and Apache Hadoop® YARN Fundamentals LiveLessons videos from Pearson, and is book co-author of Apache Hadoop® YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 and author of Hadoop® 2 Quick Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem, also from Addison-Wesley, and is author of High Performance Computing for Dummies .

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