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Practical Data Science with Hadoop and Spark : Designing and Building Effective Analytics at Scale, Paperback / softback Book

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

Paperback / softback

Available to Order - This title is available to order, with delivery expected within 2 weeks

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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 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

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