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Titlebook: Data Science for Entrepreneurship; Principles and Metho Werner Liebregts,Willem-Jan van den Heuvel,Arjan v Textbook 2023 Springer Nature Sw

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書目名稱Data Science for Entrepreneurship
副標(biāo)題Principles and Metho
編輯Werner Liebregts,Willem-Jan van den Heuvel,Arjan v
視頻videohttp://file.papertrans.cn/264/263116/263116.mp4
概述Offers a practical approach to leverage big data and AI for new value creation.Compiles detailed examples and cases on data science and its applications.Provides discussion questions at the end of eac
叢書名稱Classroom Companion: Business
圖書封面Titlebook: Data Science for Entrepreneurship; Principles and Metho Werner Liebregts,Willem-Jan van den Heuvel,Arjan v Textbook 2023 Springer Nature Sw
描述.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..
出版日期Textbook 2023
關(guān)鍵詞Applications of data science in entrepreneurship; Data science for startups; Cyberphysical systems; Dat
版次1
doihttps://doi.org/10.1007/978-3-031-19554-9
isbn_softcover978-3-031-19556-3
isbn_ebook978-3-031-19554-9Series ISSN 2662-2866 Series E-ISSN 2662-2874
issn_series 2662-2866
copyrightSpringer Nature Switzerland AG 2023
The information of publication is updating

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Sequential Experimentation and Learninglearner selects actions that will generate new data which allow for future learning. Critically, it is assumed that only the data associated with the action taken by the learner is revealed: the potential outcomes of alternative actions are not disclosed. Generally, such sequential learning approach
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Data-Driven Decision-Makingay that for securing data science impact, data science should start and end with an extensive analysis of the related decision-making. The full embedding of data science in decision-making is often labeled data-driven decision-making (DDDM). This includes the use of data and data science concepts in
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