MARC details
| 000 -LEADER |
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| fixed length control field |
04321nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250422113201.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250422b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781484267646 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.73 |
| Item number |
KAM |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Kampakis, Stylianos |
| 245 ## - TITLE STATEMENT |
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| Title |
The decision maker's handbook to data science: |
| Remainder of title |
AI and data science for non-technical executives, managers, and founders |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
Apress |
| Place of publication, distribution, etc. |
London |
| Date of publication, distribution, etc. |
2020 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
viii, 156 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
INR |
| Price amount |
499.00 |
| 500 ## - GENERAL NOTE |
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| General note |
Table of contents:<br/>Table of contents (15 chapters)<br/>Front Matter<br/>Pages i-v<br/>Download chapter PDF <br/>Demystifying Data Science and All the Other Buzzwords<br/>Stylianos Kampakis<br/>Pages 1-25<br/>Data Management<br/>Stylianos Kampakis<br/>Pages 27-33<br/>Data Collection Problems<br/>Stylianos Kampakis<br/>Pages 35-47<br/>How to Keep Data Tidy<br/>Stylianos Kampakis<br/>Pages 49-53<br/>Thinking like a Data Scientist (Without Being One)<br/>Stylianos Kampakis<br/>Pages 55-62<br/>A Short Introduction to Statistics<br/>Stylianos Kampakis<br/>Pages 63-78<br/>A Short Introduction to Machine Learning<br/>Stylianos Kampakis<br/>Pages 79-90<br/>An introduction to AI<br/>Stylianos Kampakis<br/>Pages 91-102<br/>Problem Solving<br/>Stylianos Kampakis<br/>Pages 103-110<br/>Pitfalls<br/>Stylianos Kampakis<br/>Pages 111-116<br/>Hiring and Managing Data Scientists<br/>Stylianos Kampakis<br/>Pages 117-135<br/>Building a Data Science Culture<br/>Stylianos Kampakis<br/>Pages 137-153<br/>AI Ethics<br/>Stylianos Kampakis<br/>Pages 155-164<br/>Navigating the Future of Artificial Intelligence<br/>Stylianos Kampakis<br/>Pages 165-175<br/>Epilogue: Data Science and AI Rule the World<br/>Stylianos Kampakis<br/>Pages 177-178<br/>Back Matter<br/>Pages 179-192<br/><br/>[https://link.springer.com/book/10.1007/979-8-8688-0279-9] |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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.<br/><br/><br/>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.<br/><br/>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.<br/><br/>What You Will Learn<br/><br/>Integrate AI with other innovative technologies<br/>Explore anticipated ethical, regulatory, and technical landscapes that will shape the future of AI and data science<br/>Discover how to hire and manage data scientists<br/>Build the right environment in order to make your organization data-driven<br/>Who This Book Is For<br/><br/>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.<br/><br/><br/><br/>(https://link.springer.com/book/10.1007/979-8-8688-0279-9) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Database management |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Decision making--Data processing |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
