Applying Data Science
Springer International Publishing (Verlag)
978-3-030-36374-1 (ISBN)
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.
Arthur Kordon is an internationally recognized expert in applying data science, advanced analytics and artificial intelligence in industry. The list of his current projects for large corporations includes customer churn analysis, classifying patterns in office occupancy, and nonlinear price forecasting. In his previous role as advanced analytics leader at Dow Chemical, Dr. Kordon successfully applied advanced analytics solutions in various business problems in forecasting, business cycles analysis, price elasticity analysis, and fraud detection in auditing. He also introduced novel technologies for improved manufacturing and new product design based on artificial intelligence, including robust inferential sensors, process optimization based on empirical emulators, automated operating discipline, and accelerated fundamental model building. He holds a US patent and has published more than 70 papers, 14 book chapters in the area of applied artificial intelligence and advanced analytics, and 3 books.
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.
| Erscheinungsdatum | 15.09.2020 |
|---|---|
| Zusatzinfo | XXXII, 494 p. 262 illus., 195 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 906 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Schlagworte | Advanced Analytics • Artificial Intelligence (AI) • Business Analytics • Data Mining • Data Science • Industrial AI • machine learning |
| ISBN-10 | 3-030-36374-0 / 3030363740 |
| ISBN-13 | 978-3-030-36374-1 / 9783030363741 |
| Zustand | Neuware |
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