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Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale
Contributor(s): Mendelevitch, Ofer (Author), Stella, Casey (Author), Eadline, Douglas (Author)
ISBN: 0134024141     ISBN-13: 9780134024141
Publisher: Addison-Wesley Professional
OUR PRICE:   $42.74  
Product Type: Paperback - Other Formats
Published: December 2016
Qty:
Temporarily out of stock - Will ship within 2 to 5 weeks
Additional Information
BISAC Categories:
- Computers | Databases - Data Mining
- Computers | Databases - Data Warehousing
Dewey: 006.312
Series: Addison-Wesley Data & Analytics
Physical Information: 0.8" H x 6.9" W x 9.1" (0.90 lbs) 256 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
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(R) 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
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