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Data science in higher education / Jesse Lawson

By: Material type: TextTextPublication details: Chico, CA : Jesse Lawson, c2015Description: 213 pages ; 23 cmISBN:
  • 9781515206460
Subject(s): LOC classification:
  • Q 181 .L39 2015
Contents:
What is data science? -- The data science cycle -- What is machine learning? -- Predicting numerical values with regression -- Predicting class membership with classification -- Naive Bayes Classification -- The road ahead (and looking back).
Summary: Data science in higher education is the process of turning raw institutional data into actionable intelligence. With this introduction to foundational topics in machine learning and predictive analytics, ambitious leaders in research can develop and employ sophisticated predictive models to better inform their institution's decision-making process. You don't need an advanced degree in math or statistics to do data science. With the open-source statistical programming language R, you'll learn how to tackle real-life institutional data challenges (with actual institutional data!) by going step-by-step through different case studies
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 Q 181 .L39 2015 (Browse shelf(Opens below)) c.1 Available NULIB000013805

Includes bibliographical references.

What is data science? -- The data science cycle -- What is machine learning? -- Predicting numerical values with regression -- Predicting class membership with classification -- Naive Bayes Classification -- The road ahead (and looking back).

Data science in higher education is the process of turning raw institutional data into actionable intelligence. With this introduction to foundational topics in machine learning and predictive analytics, ambitious leaders in research can develop and employ sophisticated predictive models to better inform their institution's decision-making process. You don't need an advanced degree in math or statistics to do data science. With the open-source statistical programming language R, you'll learn how to tackle real-life institutional data challenges (with actual institutional data!) by going step-by-step through different case studies

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