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Data science strategy / by Ulrika Jägare ; foreword by Lillian Pierson, CEO of Data-Mania.

Jägare, Ulrika, author. (Author). Pierson, Lillian, (writer of foreword.).

Available copies

  • 1 of 1 copy available at SPARK Libraries.

Current holds

0 current holds with 1 total copy.

Location Call Number / Copy Notes Barcode Shelving Location Status Due Date
Bethlehem Main Library 658.403 (Text) 33062009211278 New Adult Non-Fiction Available -

Record details

  • ISBN: 9781119566250
  • ISBN: 1119566258
  • Physical Description: xvi, 332 pages : illustrations ; 24 cm
  • Publisher: Hoboken, NJ : John Wiley & Sons, Inc., [2019]

Content descriptions

General Note:
Includes index.
Formatted Contents Note:
Framing data science strategy -- Considering the inherent complexity in data science -- Dealing with difficult challenges -- Managing change in data science -- Understanding the past, present, and future of data -- Knowing your data -- Considering the ethical aspects of data science -- Becoming data-driven -- Evolving from data-driven to machine-driven -- Building successful data science teams -- Approaching a data science organizational setup -- Positioning the role of the Chief Data Officer (CDO) -- Acquiring resources and competencies -- Developing a data architecture -- Focusing data governance on the right aspects -- Managing models during development and production -- Exploring the importance of open source -- Realizing the infrastructure -- Investing in data as a business -- Using data for insights or commercial opportunities -- Engaging differently with your customers -- Introducing data-driven business models -- Handling new delivery models -- Ten reasons to develop a data science strategy -- Ten mistakes to avoid when investing in data science.
Summary, etc.:
"Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the "what" and the "why" of data science and covering what it takes to lead and nurture a top-notch team of data scientists."--Amazon.
Subject: Big data.
Data mining.
Information science.
Data structures (Computer science)
Database management.

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