Applying Data Science: How to Create Value with Artificial Intelligence

ISBN-13: 9783030363741 / Angielski / Twarda / 2020 / 494 str.

Arthur K. Kordon

Spis treści

Part I, From Business Problems to Data Science.- Data Science Based on Artificial Intelligence.- Business Problems Dependent on Data.- Artificial Intelligence-Based Data Science Solutions.- Integrate and Conquer.- The Lost-in-Translation Trap.- Part II, The AI-Based Data Science Toolbox.- The AI-Based Data Science Workflow.- Problem Knowledge Acquisition.- Data Preparation.- Data Analysis.- Model Development.- The Model Deployment Life Cycle.- Part III, AI-Based Data Science in Action.- Infrastructure.- People.- Applications of AI-Based Data Science in Manufacturing.- Applications of AI-Based Data Science in Business.- How to Operate AI-Based Data Science in a Business.- How to Become an Effective Data Scientist.- Glossary.

O autorze

Dr. Arthur Kordon is an internationally recognized expert in applying data science, advanced analytics and artificial intelligence in industry. He has solved meaningful business problems with these technologies generating substantial economic effect. Dr. Kordon also has successfully advised several large and renowned enterprises on how to integrate AI into their organizations and trained their employees. He knows and has an impressive record on how businesses can create value with AI.

Blurb

This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. 

In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets.

The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.