Practical data science with Hadoop and Spark designing and building effective analytics at scale
The Complete Guide to Data Science with Hadoop--For 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. Drawi...
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[2017]
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| Schriftenreihe: | Addison-Wesley data & analytics series
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| spelling | Medelevitch, Ofer VerfasserIn aut Practical data science with Hadoop and Spark designing and building effective analytics at scale Ofer Medelevitch, Casey Stella, Douglas Eadline Designing and building effective analytics at scale Boston Addison-Wesley [2017] ©2017 1 online resource illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Addison-Wesley data & analytics series Includes index. - Includes: list of additional resources and index. - Online resource; title from title page (Safari, viewed December 16, 2016) The Complete Guide to Data Science with Hadoop--For 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. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language. Apache Hadoop Spark (Electronic resource : Apache Software Foundation) Electronic data processing Distributed processing Big data Computer programs Traitement réparti Données volumineuses ; Logiciels COMPUTERS ; Data Processing Electronic data processing ; Distributed processing Stella, Casey VerfasserIn aut Eadline, Doug 1956- VerfasserIn aut TUM01 ZDB-30-ORH TUM_PDA_ORH https://learning.oreilly.com/library/view/-/9780134029733/?ar X:ORHE Aggregator lizenzpflichtig Volltext |
| spellingShingle | Medelevitch, Ofer Stella, Casey Eadline, Doug 1956- Practical data science with Hadoop and Spark designing and building effective analytics at scale Apache Hadoop Spark (Electronic resource : Apache Software Foundation) Electronic data processing Distributed processing Big data Computer programs Traitement réparti Données volumineuses ; Logiciels COMPUTERS ; Data Processing Electronic data processing ; Distributed processing |
| title | Practical data science with Hadoop and Spark designing and building effective analytics at scale |
| title_alt | Designing and building effective analytics at scale |
| title_auth | Practical data science with Hadoop and Spark designing and building effective analytics at scale |
| title_exact_search | Practical data science with Hadoop and Spark designing and building effective analytics at scale |
| title_full | Practical data science with Hadoop and Spark designing and building effective analytics at scale Ofer Medelevitch, Casey Stella, Douglas Eadline |
| title_fullStr | Practical data science with Hadoop and Spark designing and building effective analytics at scale Ofer Medelevitch, Casey Stella, Douglas Eadline |
| title_full_unstemmed | Practical data science with Hadoop and Spark designing and building effective analytics at scale Ofer Medelevitch, Casey Stella, Douglas Eadline |
| title_short | Practical data science with Hadoop and Spark |
| title_sort | practical data science with hadoop and spark designing and building effective analytics at scale |
| title_sub | designing and building effective analytics at scale |
| topic | Apache Hadoop Spark (Electronic resource : Apache Software Foundation) Electronic data processing Distributed processing Big data Computer programs Traitement réparti Données volumineuses ; Logiciels COMPUTERS ; Data Processing Electronic data processing ; Distributed processing |
| topic_facet | Apache Hadoop Spark (Electronic resource : Apache Software Foundation) Electronic data processing Distributed processing Big data Computer programs Traitement réparti Données volumineuses ; Logiciels COMPUTERS ; Data Processing Electronic data processing ; Distributed processing |
| url | https://learning.oreilly.com/library/view/-/9780134029733/?ar |
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