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

By:

Kampakis, Stylianos

Material type: Text

TextPublication details: Apress London 2020Edition: 2ndDescription: viii, 156 pISBN:

9781484267646

Subject(s):

DDC classification:

005.73 KAM

Summary: 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 issues surrounding 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. What You Will Learn Integrate AI with other innovative technologies Explore anticipated ethical, regulatory, and technical landscapes that will shape the future of AI and data science Discover how to hire and manage data scientists Build the right environment in order to make your organization data-driven Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science. (https://link.springer.com/book/10.1007/979-8-8688-0279-9)

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	005.73 KAM (Browse shelf(Opens below))	Available		008272

Table of contents:
Table of contents (15 chapters)
Front Matter
Pages i-v
Download chapter PDF
Demystifying Data Science and All the Other Buzzwords
Stylianos Kampakis
Pages 1-25
Data Management
Stylianos Kampakis
Pages 27-33
Data Collection Problems
Stylianos Kampakis
Pages 35-47
How to Keep Data Tidy
Stylianos Kampakis
Pages 49-53
Thinking like a Data Scientist (Without Being One)
Stylianos Kampakis
Pages 55-62
A Short Introduction to Statistics
Stylianos Kampakis
Pages 63-78
A Short Introduction to Machine Learning
Stylianos Kampakis
Pages 79-90
An introduction to AI
Stylianos Kampakis
Pages 91-102
Problem Solving
Stylianos Kampakis
Pages 103-110
Pitfalls
Stylianos Kampakis
Pages 111-116
Hiring and Managing Data Scientists
Stylianos Kampakis
Pages 117-135
Building a Data Science Culture
Stylianos Kampakis
Pages 137-153
AI Ethics
Stylianos Kampakis
Pages 155-164
Navigating the Future of Artificial Intelligence
Stylianos Kampakis
Pages 165-175
Epilogue: Data Science and AI Rule the World
Stylianos Kampakis
Pages 177-178
Back Matter
Pages 179-192

[https://link.springer.com/book/10.1007/979-8-8688-0279-9]

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 issues surrounding 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.

What You Will Learn

Integrate AI with other innovative technologies
Explore anticipated ethical, regulatory, and technical landscapes that will shape the future of AI and data science
Discover how to hire and manage data scientists
Build the right environment in order to make your organization data-driven
Who This Book Is For

Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.

(https://link.springer.com/book/10.1007/979-8-8688-0279-9)

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