Data science / by Lillian Pierson ; foreword by Jake Porway, founder and executive director of DataKind.

By: Pierson, Lillian [ ]Contributor(s): Porway, Jake [ ]Material type: TextTextPublisher: Hoboken, NJ : John Wiley and Sons, Inc., c2017Edition: 2nd edDescription: xvi, 364 pages : illustrations, charts ; 24 cmISBN: 9781119327639Other title: Data science for dummiesSubject(s): Information retrieval | Data miningDDC classification: 004
Contents:
Part 1: Getting Started with Data Science----------- Part 2: Using Data Science to Extract Meaning from your Data------------ Part 3: Creating Data Visualization that Clearly Communicate Meaning------------ Part 4: Computing for Data Science------------ Part 5: Applying domain Expertise to solve Real World Problem using Data Science---------- Part 6: The Part of Tens--------------
Summary: 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 location Call number Status Date due Barcode Item holds
Books Books Namal Library
Computer Science
004 PIE-D 2017 10285 (Browse shelf) Available 0010285
Total holds: 0

Includes index.

Part 1: Getting Started with Data Science-----------
Part 2: Using Data Science to Extract Meaning from your Data------------
Part 3: Creating Data Visualization that Clearly Communicate Meaning------------
Part 4: Computing for Data Science------------
Part 5: Applying domain Expertise to solve Real World Problem using Data Science----------
Part 6: The Part of Tens--------------

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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