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Data science for entrepreneurshipprinciples and methods for data engineering, analytics, entrepreneurship, and the society /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science for entrepreneurshipedited by Werner Liebregts, Willem-Jan van den Heuvel, Arjan van den Born.
Reminder of title:	principles and methods for data engineering, analytics, entrepreneurship, and the society /
other author:	Liebregts, Werner.
Published:	Cham :Springer International Publishing :2023.
Description:	xiv, 532 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	EntrepreneurshipData processing.
Online resource:	https://doi.org/10.1007/978-3-031-19554-9
ISBN:	9783031195549$q(electronic bk.)

Data science for entrepreneurshipprinciples and methods for data engineering, analytics, entrepreneurship, and the society /
Data science for entrepreneurshipprinciples and methods for data engineering, analytics, entrepreneurship, and the society /[electronic resource] :edited by Werner Liebregts, Willem-Jan van den Heuvel, Arjan van den Born. - Cham :Springer International Publishing :2023. - xiv, 532 p. :ill., digital ;24 cm. - Classroom companion: Business,2662-2874. - Classroom companion.Business..

The Unlikely Wedlock Between Data Science and Entrepreneurship -- Data Engineering: Big Data Engineering -- Data Governance -- Big Data Architectures -- Data Engineering in Action -- Data Analytics: Supervised Machine Learning in a Nutshell -- An Intuitive Introduction to Deep Learning -- Sequential Experimentation and Learning -- Advanced Analytics on Complex Industrial Data -- Data Analytics in Action -- Data Entrepreneurship -- Data-Driven Decision Making -- Digital Entrepreneurship -- Strategy in the Era of Digital Disruption -- Digital Servitization in Agriculture -- Entrepreneurial Finance -- Entrepreneurial Marketing -- Data and Society: Data Protection Law and Responsible Data Science -- Perspectives from Intellectual Property Law -- Liability and Contract Issues Regarding Data -- Data Ethics and Data Science -- Value Sensitive Software Design -- Data Science for Entrepreneurship: The Road Ahead.

The fast-paced technological development and the plethora of data create numerous opportunities waiting to be exploited by entrepreneurs. This book provides a detailed, yet practical, introduction to the fundamental principles of data science and how entrepreneurs and would-be entrepreneurs can take advantage of it. It walks the reader through sections on data engineering, and data analytics as well as sections on data entrepreneurship and data use in relation to society. The book also offers ways to close the research and practice gaps between data science and entrepreneurship. By having read this book, students of entrepreneurship courses will be better able to commercialize data-driven ideas that may be solutions to real-life problems. Chapters contain detailed examples and cases for a better understanding. Discussion points or questions at the end of each chapter help to deeply reflect on the learning material.

ISBN: 9783031195549$q(electronic bk.)

Standard No.: 10.1007/978-3-031-19554-9doiSubjects--Topical Terms:

949630
Entrepreneurship
--Data processing.

LC Class. No.: HD62.5

Dewey Class. No.: 658.14

Data science for entrepreneurshipprinciples and methods for data engineering, analytics, entrepreneurship, and the society /
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520 $a The fast-paced technological development and the plethora of data create numerous opportunities waiting to be exploited by entrepreneurs. This book provides a detailed, yet practical, introduction to the fundamental principles of data science and how entrepreneurs and would-be entrepreneurs can take advantage of it. It walks the reader through sections on data engineering, and data analytics as well as sections on data entrepreneurship and data use in relation to society. The book also offers ways to close the research and practice gaps between data science and entrepreneurship. By having read this book, students of entrepreneurship courses will be better able to commercialize data-driven ideas that may be solutions to real-life problems. Chapters contain detailed examples and cases for a better understanding. Discussion points or questions at the end of each chapter help to deeply reflect on the learning material.
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