東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

The decision maker's handbook to data science = AI and data science for non-technical executives, managers, and founders /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The decision maker's handbook to data science/ by Stylianos Kampakis.
Reminder of title:	AI and data science for non-technical executives, managers, and founders /
Author:	Kampakis, Stylianos.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	v, 192 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Demystifying Data Science, AI and All the Other Buzzwords -- Chapter 2: Data Management -- Chapter 3: Data Collection Problems -- Chapter 4: How to Keep Data Tidy -- Chapter 5: Thinking like a Data Scientist (Without Being One) -- Chapter 6: A Short Introduction to Statistics -- Chapter 7: A Short Introduction to Machine Learning -- Chapter 8: An introduction to AI -- Chapter 9: Problem Solving -- Chapter 10: Pitfalls -- Chapter 11: Hiring and Managing Data Scientists -- Chapter 12: Building a Data-Driven Culture -- Chapter 13: AI Ethics -- Chapter 14: The Future of AI and Data Science. Epilogue: Data Science Rules the World -- Appendix: Tools for Data Science.
Contained By:	Springer Nature eBook
Subject:	Decision making - Data processing. -
Online resource:	https://doi.org/10.1007/979-8-8688-0279-9
ISBN:	9798868802799

The decision maker's handbook to data science = AI and data science for non-technical executives, managers, and founders /
Kampakis, Stylianos.

The decision maker's handbook to data scienceAI and data science for non-technical executives, managers, and founders /[electronic resource] :by Stylianos Kampakis. - Third edition. - Berkeley, CA :Apress :2024. - v, 192 p. :ill., digital ;24 cm.

Chapter 1: Demystifying Data Science, AI and All the Other Buzzwords -- Chapter 2: Data Management -- Chapter 3: Data Collection Problems -- Chapter 4: How to Keep Data Tidy -- Chapter 5: Thinking like a Data Scientist (Without Being One) -- Chapter 6: A Short Introduction to Statistics -- Chapter 7: A Short Introduction to Machine Learning -- Chapter 8: An introduction to AI -- Chapter 9: Problem Solving -- Chapter 10: Pitfalls -- Chapter 11: Hiring and Managing Data Scientists -- Chapter 12: Building a Data-Driven Culture -- Chapter 13: AI Ethics -- Chapter 14: The Future of AI and Data Science. Epilogue: Data Science Rules the World -- Appendix: Tools for Data Science.

Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI's ability to emulate human decision-making. Author Stylianos Kampakis introduces you to the critical aspect of ethics in AI, an area of growing importance and scrutiny. The narrative examines the ethical considerations intrinsic to the development and deployment of AI technologies, including bias, fairness, transparency, and accountability. You'll be provided with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issuessurrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated edition also includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists. Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker's Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide.

ISBN: 9798868802799

Standard No.: 10.1007/979-8-8688-0279-9doiSubjects--Topical Terms:

752376
Decision making
--Data processing.

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.73

The decision maker's handbook to data science = AI and data science for non-technical executives, managers, and founders /
LDR:03599nmm a2200361 a 4500 001 2372725
003 DE-He213
005 20240701125240.0
006 m d
007 cr nn 008maaau
008 241127s2024 cau s 0 eng d
020 $a 9798868802799 $q (electronic bk.)
020 $a 9798868802782 $q (paper)
024 7 $a 10.1007/979-8-8688-0279-9 $2 doi
035 $a 979-8-8688-0279-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D35
072 7 $a UMB $2 bicssc
072 7 $a GPF $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UMB $2 thema
072 7 $a GPF $2 thema
082 0 4 $a 005.73 $2 23
090 $a QA76.9.D35 $b K15 2024
100 1 $a Kampakis, Stylianos. $3 3445836
245 1 4 $a The decision maker's handbook to data science $h [electronic resource] : $b AI and data science for non-technical executives, managers, and founders / $c by Stylianos Kampakis.
250 $a Third edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2024.
300 $a v, 192 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Demystifying Data Science, AI and All the Other Buzzwords -- Chapter 2: Data Management -- Chapter 3: Data Collection Problems -- Chapter 4: How to Keep Data Tidy -- Chapter 5: Thinking like a Data Scientist (Without Being One) -- Chapter 6: A Short Introduction to Statistics -- Chapter 7: A Short Introduction to Machine Learning -- Chapter 8: An introduction to AI -- Chapter 9: Problem Solving -- Chapter 10: Pitfalls -- Chapter 11: Hiring and Managing Data Scientists -- Chapter 12: Building a Data-Driven Culture -- Chapter 13: AI Ethics -- Chapter 14: The Future of AI and Data Science. Epilogue: Data Science Rules the World -- Appendix: Tools for Data Science.
520 $a Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI's ability to emulate human decision-making. Author Stylianos Kampakis introduces you to the critical aspect of ethics in AI, an area of growing importance and scrutiny. The narrative examines the ethical considerations intrinsic to the development and deployment of AI technologies, including bias, fairness, transparency, and accountability. You'll be provided with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issuessurrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated edition also includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists. Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker's Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide.
650 0 $a Decision making $x Data processing. $3 752376
650 0 $a Artificial intelligence. $3 516317
650 0 $a Big data. $3 2045508
650 0 $a Database management. $3 527442
650 1 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Big Data. $3 3134868
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-0279-9
950 $a Professional and Applied Computing (SpringerNature-12059)