Data science / by Lillian Pierson ; foreword by Jake Porway.

By: Contributor(s): Material type: TextTextSeries: --For dummiesPublication details: Hoboken, NJ : John Wiley and Sons, Inc., [2017].Edition: 2nd editionDescription: xvi, 364 pages: illustrations, charts; 24 cmContent type:
  • still image sti|2rdacontent.
Media type:
  • unmediated c|2rdamedia.
Carrier type:
  • volume cr|2rdacarrier.
ISBN:
  • 9781119327639
Uniform titles:
  • Data science for dummies.
Subject(s): LOC classification:
  • 004 P61d c2017
Contents:
Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations.
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Item type Current library Call number Status Date due Barcode
Books Books FEU - Cavite Basic Education BED Library CIR 004 P61d c2017 (Browse shelf(Opens below)) Checked out 05/19/2022 00:00 HS1714

in English

Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations.

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