Practical Data Science with Hadoop and Spark (Addison Wesley)
Thursday, 12 January 2017

This book shows how to solve real data science problems with Hadoop and Spark. Authors Ofer Mendelevitch, Casey Stella, and Douglas Eadline draw on their experience with Hadoop and big data to bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials.

<ASIN:0134024141>

The essentials of data science and the modern Hadoop ecosystem are introduced, along with guidance on data ingestion, data cleansing, and visualization.

The book then moves on to focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP).

Author: Ofer Mendelevitch, Casey Stella, and Douglas Eadline
Publisher: Addison Wesley
Date: December 2016
Pages: 256
ISBN: 978-0134024141
Print: 0134024141
Kindle: B01N7G1M8J
Audience: Data mining developers
Level: Intermediate
Category: Data Science

 

Covers: 

  • 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

 

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