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Why Data Science Projects Fail : The Harsh Realities of Implementing AI and Analytics, without the Hype (Chapman & Hall/CRC Data Science Series)

By: Douglas Gray (Author) , Evan Shellshear (Author)

3 in stock

Ksh 9,350.00

Format: Paperback or Softback

ISBN-10: 1032660309

ISBN-13: 9781032660301

Collection / Series: Chapman & Hall/CRC Data Science Series

Collection Type: Publisher collection

Publisher: Taylor & Francis Ltd

Imprint: Chapman & Hall/CRC

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Sep 5th, 2024

Publication Status: Active

Product extent: 208 Pages

Weight: 338.00 grams

Dimensions (height x width x thickness): 23.20 x 15.70 x 1.60 cms

Product Classification / Subject(s): Relational databases
Artificial intelligence

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The field of artificial intelligence, data science and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven.

The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven.

This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether.

For the first time, business leaders, practitioners, students, and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics.


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