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

Ofer Mendelevitch, Casey Stella, Douglas Eadline

  • 出版商: Addison Wesley
  • 出版日期: 2016-12-12
  • 售價: $1,780
  • 貴賓價: 9.5$1,691
  • 語言: 英文
  • 頁數: 256
  • 裝訂: Paperback
  • ISBN: 0134024141
  • ISBN-13: 9780134024141
  • 相關分類: HadoopSpark資料科學

下單後立即進貨 (約1週~2週)

相關主題

商品描述

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