What Every Engineer Should Know about Data-Driven Analytics
暫譯: 每位工程師應該了解的數據驅動分析知識
Srinivasan, Satish Mahadevan, Laplante, Phillip A.
- 出版商: CRC
- 出版日期: 2023-04-13
- 售價: $2,550
- 貴賓價: 9.5 折 $2,423
- 語言: 英文
- 頁數: 260
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1032235403
- ISBN-13: 9781032235400
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商品描述
What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains.
- Utilizes practical examples from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making.
- Introduces various approaches to build models that exploits different algorithms.
- Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets.
- Explores the augmentation of technical and mathematical materials with explanatory worked examples.
- Includes a glossary, self-assessments, and worked-out practice exercises.
Written to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science.
商品描述(中文翻譯)
《每位工程師應該了解的數據驅動分析》提供了對於預測數據分析中使用的機器學習理論概念和方法的全面介紹。通過介紹理論並提供實際應用，這本書可以被各個工程學科所理解。它詳細且專注地處理了重要的機器學習方法和概念，這些方法和概念可以用來建立模型，以促進不同領域的決策制定。
- 利用來自不同工程學科和其他相關技術領域的實際範例，展示如何從數據到洞察，再到決策制定的過程。
- 介紹了利用不同算法建立模型的各種方法。
- 討論了可以通過機器學習建立的預測模型，並用於從大型數據集中挖掘模式。
- 探索了用解釋性範例增強技術和數學材料的方式。
- 包含詞彙表、自我評估和詳細的練習題。
這本全面的入門書籍旨在讓非專家也能輕鬆理解，適合工程和數據科學領域的學生、專業人士和研究人員。
作者簡介
Satish M. Srinivasan received his B.E. in Information Technology from Bharathidasan University, India and M.S. in Industrial Engineering and Management from the Indian Institute of Technology Kharagpur, India. He earned his Ph.D. in Information Technology from the University of Nebraska at Omaha. Prior to joining Penn State Great Valley, he worked as a postdoctoral research associate at University of Nebraska Medical Center, Omaha. Dr. Srinivasan teaches courses related to database design, data mining, data collection and cleaning, computer, network and web securities, and business process management. His research interests include data aggregation in partially connected networks, fault-tolerance, software engineering, social network analysis, data mining, machine learning, Big Data, and predictive analytics and bioinformatics.
Phil Laplante is Professor of Software and Systems Engineering at The Pennsylvania State University. He received his B.S., M.Eng., and Ph.D. from Stevens Institute of Technology and an MBA from the University of Colorado. He is a Fellow of the IEEE and SPIE and has won international awards for his teaching, research, and service. From 2010 to 2017 he led the effort to develop a national licensing exam for software engineers.
He has worked in avionics, CAD, and software testing systems and he has published 40 books and more than 300 scholarly papers. He is a licensed professional engineer in the Commonwealth of Pennsylvania. He is also a frequent technology advisor to senior executives, investors, entrepreneurs, and attorneys and actively serves on corporate technology advisory boards.
His research interests include artificial intelligent systems, critical systems, requirements engineering, and software quality and management. Prior to his appointment at Penn State he was a software development professional, technology executive, college president, and entrepreneur.
作者簡介(中文翻譯)
Satish M. Srinivasan 於印度 Bharathidasan 大學獲得資訊科技學士學位，並於印度 Kharagpur 的印度理工學院獲得工業工程與管理碩士學位。他在內布拉斯加州奧馬哈的內布拉斯加大學獲得資訊科技博士學位。在加入賓州州立大學大谷校區之前，他曾在內布拉斯加州醫學中心擔任博士後研究助理。Srinivasan 博士教授與資料庫設計、資料探勘、資料收集與清理、計算機、網路與網頁安全以及業務流程管理相關的課程。他的研究興趣包括部分連接網路中的資料聚合、容錯、軟體工程、社交網路分析、資料探勘、機器學習、大數據、預測分析和生物資訊學。
Phil Laplante 是賓州州立大學的軟體與系統工程教授。他於史蒂文斯理工學院獲得學士、碩士及博士學位，並於科羅拉多大學獲得工商管理碩士學位。他是 IEEE 和 SPIE 的會士，並因其教學、研究和服務獲得國際獎項。從 2010 年到 2017 年，他主導了為軟體工程師開發國家執照考試的工作。
他曾在航空電子、計算機輔助設計 (CAD) 和軟體測試系統方面工作，並出版了 40 本書籍和 300 多篇學術論文。他是賓夕法尼亞州的執業專業工程師，並且經常擔任高層主管、投資者、企業家和律師的技術顧問，並積極參與企業技術諮詢委員會。
他的研究興趣包括人工智慧系統、關鍵系統、需求工程以及軟體質量與管理。在被任命為賓州州立大學之前，他曾是一名軟體開發專業人士、技術高管、大學校長和企業家。
