國立高雄大學圖資館 |

登入

回首頁

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science MBAby Alex Coad.
其他題名:	big data, digitalization, and strategy; with applications in R /
作者:	Coad, Alex.
出版者:	Singapore :Springer Nature Singapore :2025.
面頁冊數:	xxi, 171 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Quantitative research.
電子資源:	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 /
LDR:03009nmm a2200337 a 4500 001 692224
003 DE-He213
005 20251125120405.0
006 m d
007 cr nn 008maaau
008 260604s2025 si s 0 eng d
020 $a 9789819524334$q(electronic bk.)
020 $a 9789819524327$q(paper)
024 7 $a 10.1007/978-981-95-2433-4 $2 doi
035 $a 978-981-95-2433-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.Q36
072 7 $a KJQ $2 bicssc
072 7 $a COM005030 $2 bisacsh
072 7 $a KJQ $2 thema
082 0 4 $a 001.42 $2 23
090 $a QA76.9.Q36 $b C652 2025
100 1 $a Coad, Alex. $3 976968
245 1 0 $a Data science MBA $h [electronic resource] : $b big data, digitalization, and strategy; with applications in R / $c by Alex Coad.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xxi, 171 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer texts in business and economics, $x 2192-4341
505 0 $a 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.
520 $a 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.
650 0 $a Quantitative research. $3 367894
650 0 $a Big data. $3 609582
650 0 $a Data mining. $3 184440
650 0 $a Artificial intelligence $x Business applications. $3 996997
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Business Analytics. $3 919294
650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 913173
650 2 4 $a Data Analysis and Big Data. $3 913147
650 2 4 $a Data Science. $3 913495
650 2 4 $a Computer Application in Administrative Data Processing. $3 914917
650 2 4 $a Business Strategy and Leadership. $3 914887
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer texts in business and economics. $3 557968
856 4 0 $u https://doi.org/10.1007/978-981-95-2433-4
950 $a Business and Management (SpringerNature-41169)