Why Data Science Projects Fail : The Harsh Realities of Implementing AI and Analytics, Without the Hype

Autor:	Douglas Gray, Evan Shellshear
Předmět:	Big data, Quantitative research, Artificial intelligence, Decision making--Mathematical models
Kategorie:	COMPUTERS / Artificial Intelligence / General, COMPUTERS / Business & Productivity Software / Business Intelligence, COMPUTERS / Computer Science, COMPUTERS / Data Science / General, COMPUTERS / Database Administration & Management, COMPUTERS / Data Science / Data Analytics, COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / Data Visualization, COMPUTERS / Data Science / Machine Learning
Popis:	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.
Databáze:	eBook Index
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