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Robust quality : powerful integration of data science and process engineering

Author: Rajesh Jugulum
Publisher: Boca Raton, FL : CRC Press, [2019] ©2019
Series: Continuous improvement series.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Rajesh Jugulum
ISBN: 9780429877261 0429877269
OCLC Number: 1049150050
Description: 1 online resource (xvii, 124 pages) : illustrations
Contents: The importance of data quality and process quality --
Data science and process engineering concepts --
Building data and process strategy and metrics management --
Robust quality--an integrated approach for ensuring overall quality --
Robust quality for analytics --
Case studies.
Series Title: Continuous improvement series.
Responsibility: Rajesh Jugulum.

Abstract:

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.

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