Robust Quality
Powerful Integration of Data Science and Process Engineering
Published on 3. September 2018
142 pages
978-0-429-87726-1 (ISBN)
Description
Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies.
Features:
Integrates data science, analytics and process engineering concepts
Discusses how to create value by considering data, analytics and processes
Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches
Reviews a structured approach for analytics execution
Features:
Integrates data science, analytics and process engineering concepts
Discusses how to create value by considering data, analytics and processes
Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches
Reviews a structured approach for analytics execution
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
37 Tables, black and white; 74 Illustrations, black and white
File size
13,98 MB
ISBN-13
978-0-429-87726-1 (9780429877261)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
03/2021
1st Edition
CRC Press
€73.70
Shipment within 15-20 days
Book
08/2018
1st Edition
CRC Press
€131.70
Shipment within 15-20 days
Person
Rajesh Jugulum, PhD, is the Informatics Director at Cigna. Prior to joining Cigna, he held executive positions in the areas of process engineering and data science at Citi Group and Bank of America. Rajesh completed his PhD under the guidance of Dr. Genichi Taguchi. Before joining the financial industry, Rajesh was at Massachusetts Institute of Technology where he was involved in research and teaching. He currently teaches at Northeastern University in Boston. Rajesh is the author/co-author of several papers and four books including books on data quality and design for Six Sigma. Rajesh is an American Society for Quality (ASQ) Fellow and his other honors include ASQ's Feigenbaum medal and International Technology Institute's Rockwell medal. Rajesh has delivered talks as the keynote speaker at several conferences, symposiums, and events related to data analytics and process engineering. He has also delivered lectures in several universities/companies across the globe and participated as a judge in data-related competitions.
Content
Chapter 1 The Importance of Data Quality and Process Quality Chapter 2 Data Science and Process Engineering Concepts Chapter 3 Building Data and Process Strategy and Metrics Management Chapter 4 Robust Quality-An Integrated Approach for Ensuring Overall Quality Chapter 5 Robust Quality for Analytics Chapter 6 Case Studies Appendix I: Control Chart Equations and Selection Approach Appendix II: Orthogonal Arrays Appendix III: Mean Square Deviation (MSD), Signal-to-Noise Ratio (SNR), and Robust Quality Index (RQI)
