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Doing data science / Rachel Schutt and Cathy O'Neil.

By: Contributor(s): Material type: TextTextPublication details: [Place of publication not identifed] : [publisher not identified], c2014Description: xxiv, 375 pages : illustrations ; 23 cmISBN:
  • 9781449358655
Subject(s): LOC classification:
  • QA 76.9 .S37 2014
Contents:
Introduction : what is data science? -- Statistical inference, exploratory data analysis, and the data science process -- Algorithms -- Spam filters, Naive Bayes, and wrangling -- Logistic regression -- Time stamps and financial modeling -- Extracting meaning from data -- Recommendation engine : building a user-facing data product -- Data visualization and fraud detection -- Social networks and data journalism -- Causality -- Epidemiology -- Lessons learned from data competitions -- Data engineering -- The Students speak -- Next-generation data scientists, Hubris and ethics.
Summary: A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering
Item type: Books
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Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Graduate Studies General Circulation Gen. Ed. - CCIT GC QA 76.9 .S37 2014 (Browse shelf(Opens below)) c.1 Available NULIB000013801

Includes index.

Introduction : what is data science? -- Statistical inference, exploratory data analysis, and the data science process -- Algorithms -- Spam filters, Naive Bayes, and wrangling -- Logistic regression -- Time stamps and financial modeling -- Extracting meaning from data -- Recommendation engine : building a user-facing data product -- Data visualization and fraud detection -- Social networks and data journalism -- Causality -- Epidemiology -- Lessons learned from data competitions -- Data engineering -- The Students speak -- Next-generation data scientists, Hubris and ethics.

A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering

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