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Data science MBAbig data, digitalization, and strategy; with applications in R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science MBAby Alex Coad.
Reminder of title:	big data, digitalization, and strategy; with applications in R /
Author:	Coad, Alex.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xxi, 171 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Quantitative research.
Online resource:	https://doi.org/10.1007/978-981-95-2433-4
ISBN:	9789819524334$q(electronic bk.)

Data science MBAbig data, digitalization, and strategy; with applications in R /
Coad, Alex.

Data science MBAbig data, digitalization, and strategy; with applications in R /[electronic resource] :by Alex Coad. - Singapore :Springer Nature Singapore :2025. - xxi, 171 p. :ill., digital ;24 cm. - Springer texts in business and economics,2192-4341. - Springer texts in business and economics..

Contents -- Preface -- Chapter 1 Introduction and definitions -- Chapter 2 Digital Transformation of Organizations -- Chapter 3 Big Data technologies and Architecture -- Chapter 4 the Data Science process -- Chapter 5 Ethics of Data Science and AI -- Chapter 6 Working with Data -- Chapter 7 The User Experience UEX -- Chapter 8 Data Visualization -- Chapter 9 Descriptions statistical associations -- Chapter 10 Prediction -- Chapter 11 Text as data -- Chapter 12 Causal inference -- Chapter 13 Conclusion -- References.

This text book focuses on what could be the most important challenge for firms to boost long-term productivity and competitiveness: digital strategy. It seeks to provide readers with a solid knowledge of the most relevant issues and concepts, that will be relevant to MBA students in real-world settings. The book discusses theoretical concepts relating to digital strategy, while also using hands-on data analysis in R software to illustrate some fundamental features and pitfalls of working with real-world data. The book starts by clarifying the meaning of relevant concepts (digitization vs digitalization; Machine learning, Artificial Intelligence), presents three leading models of digital transformation, and explains how digitalization has far-reaching implications for how organizations need to be structured. Then the book discusses the skills of a data scientist, and how digital transformation leads to new concerns surrounding ethics. Other themes include data quality, data pre-processing, data visualization, as well as the distinction between prediction and causal inference. Many of these themes are illustrated using R examples, that familiarize the reader with data analysis, using these hands-on experiences to uniquely illustrate some important themes surrounding statistical analysis, and to let readers see for themselves how some popular statistical and data science techniques actually work.

ISBN: 9789819524334$q(electronic bk.)

Standard No.: 10.1007/978-981-95-2433-4doiSubjects--Topical Terms:

367894
Quantitative research.

LC Class. No.: QA76.9.Q36

Dewey Class. No.: 001.42

Data science MBAbig data, digitalization, and strategy; with applications in R /
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