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Data Science: The Executive Summary - A Technical Book for Non-Technical Professionals

Data Science: The Executive Summary - A Technical Book for Non-Technical Professionals

Authors
Publisher John Wiley and Sons Ltd
Year 18/02/2021
Pages 208
Version hardback
Readership level Professional and scholarly
ISBN 9781119544081
Categories Mathematics
$100.64 (with VAT)
370.00 PLN / €82.51 / £70.88
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Book description

Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary - A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the "business side" of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring them When Big Data is the best approach to use for a project, and when it actually ties analysts' hands Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems How many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.

Data Science: The Executive Summary - A Technical Book for Non-Technical Professionals

Table of contents

Introduction


Chapter 1: The Business Side of Data Science


Chapter 2: Working with Modern Data


Chapter 3: Telling the Story, Summarizing the Data


Chapter 4: Machine Learning


Chapter 5: Knowing the Tools


Chapter 6: Deep Learning and Artificial Intelligence


Postscript


Index

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