Engineering data science is an emerging interdisciplinary field that leverages data-driven techniques and engineering principles to solve complex problems, optimise processes, and foster innovation. This paper provides a broad entry point to data science for engineering practitioners and demonstrates how data science is augmenting the understanding and modelling of complex systems.
This paper emphasises the important role that data science is now playing in engineering fields before going onto describe the data science process and discuss the distinctive qualities of engineering data. Examples of value propositions and applications of data science across the lifecycle are provided. To conclude, guidance is provided on how to get started with engineering data science and machine learning.
| Section | Title | Page |
|---|---|---|
| 1 | Introduction | 1 |
| 1.1 | Definitions | 2 |
| 1.2 | The Role of Data Science | 4 |
| 2 | The Process of Data Science | 6 |
| 2.1 | Business Understanding | 7 |
| 2.2 | Data Understanding | 8 |
| 2.3 | Data Preparation | 8 |
| 2.4 | Modelling | 9 |
| 2.5 | Evaluation | 9 |
| 2.6 | Deployment | 10 |
| 3 | Engineering Data - Attributes & Methods | 12 |
| 3.1 | Size of Data Sets | 12 |
| 3.2 | Quality | 12 |
| 3.3 | Outliers | 12 |
| 3.4 | Geometric Format | 13 |
| 3.5 | Multi-Dimensionality | 13 |
| 3.6 | Skewness | 13 |
| 3.7 | Sparsity of Crucial Events and Imbalanced Datasets | 13 |
| 3.8 | The Importance of Context | 14 |
| 4 | The Value of Engineering Data Science | 15 |
| 4.1 | Design | 15 |
| 4.2 | Manufacturing | 16 |
| 4.3 | Operational | 17 |
| 5 | Recommendations | 19 |
| 5.1 | Resources | 21 |
| 6 | Acronyms and Abbreviations | 23 |
| 7 | References | 24 |
International Standard Book Number: 978-1-83979-229-8
First Published: 2024
Order Reference:
Published By:
NAFEMS
Springwood - Booths Park
Chelford Road
Knutsford
WA16 8QZ
United Kingdom
Copyright © NAFEMS 2024. All rights reserved. No part of this publication may be reproduced or distributed in any form or by any means, or stored in a data base or retrieval system without prior permission of the publisher.
| Reference | HT56 |
|---|---|
| Authors | Amode. C Buchholz. C DePauw. T Pinon Fischer. O Karl. A Mooney. S |
| Language | English |
| Audiences | Analyst Manager |
| Type | Publication |
| Date | 3rd September 2024 |
| Organisation | NAFEMS |
| Region | Global |
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