Engineering Data Analytics
Seiten
2024
Cognella, Inc (Verlag)
979-8-8233-4585-9 (ISBN)
Cognella, Inc (Verlag)
979-8-8233-4585-9 (ISBN)
Delve into the world of engineering data analytics, where fundamental models intersect with probability and statistical methods to manage complex systems. Blending theory with practical Python implementations, this guide navigates experimental design, variance analysis, and quality control for real-world challenges.
Engineering Data Analytics introduces students to foundational concepts within the discipline, the centrality of models in analysis methodologies, and the significance of probability in dealing with uncertain quantities. The textbook provides engineering students with the skillsets necessary to evaluate complex systems—whether physical or operational—when closed form or simulation approaches are unavailable or inadequate, as is often the case.
The book sheds light on the complex tapestry of engineering data analytics, covering statistical quality control to experimental design strategies. It offers a practical approach by including Python code for implementing various analytical models, illustrating the intersection of theoretical understanding with practical application. Key topics such as probability mass functions, cumulative distribution functions, and the interpretation of ANOVA using the concept of sample variance are given due attention to ensure a comprehensive coverage of the subject matter.
Engineering Data Analytics is designed to support coursework at the undergraduate level and is suitable for students who are pursuing degrees in engineering disciplines that necessitate a solid grasp of data analytics principles. It can also serve as a fundamental resource for graduate-level studies, where a more profound dive into the mechanisms and advanced applications of engineering data analytics is required.
Engineering Data Analytics introduces students to foundational concepts within the discipline, the centrality of models in analysis methodologies, and the significance of probability in dealing with uncertain quantities. The textbook provides engineering students with the skillsets necessary to evaluate complex systems—whether physical or operational—when closed form or simulation approaches are unavailable or inadequate, as is often the case.
The book sheds light on the complex tapestry of engineering data analytics, covering statistical quality control to experimental design strategies. It offers a practical approach by including Python code for implementing various analytical models, illustrating the intersection of theoretical understanding with practical application. Key topics such as probability mass functions, cumulative distribution functions, and the interpretation of ANOVA using the concept of sample variance are given due attention to ensure a comprehensive coverage of the subject matter.
Engineering Data Analytics is designed to support coursework at the undergraduate level and is suitable for students who are pursuing degrees in engineering disciplines that necessitate a solid grasp of data analytics principles. It can also serve as a fundamental resource for graduate-level studies, where a more profound dive into the mechanisms and advanced applications of engineering data analytics is required.
David S. Kim is professor emeritus in the College of Engineering at Oregon State University, where he taught probability and statistics-based engineering courses in industrial and manufacturing engineering.
| Erscheinungsdatum | 05.11.2024 |
|---|---|
| Verlagsort | San Diego |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Technik | |
| ISBN-13 | 979-8-8233-4585-9 / 9798823345859 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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