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.
Table of Content
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
About the author
Field Cady, is a data scientist and author in the Seattle area. Most of his career has focused on consulting, for clients of all sizes in a range of industries. More recently he focused on using AI to mine scientific literature at the Allen Institute for Artificial Intelligence. His previous book, The Data Science Handbook, was published in 2017. His work has been covered in Wired, MIT Press and the Wall Street Journal among others.
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