Foundations of Data Science for Engineering Problem Solving

Foundations of Data Science for Engineering Problem Solving

EnglishHardbackPrint on demand
Mahalle Parikshit Narendra
Springer Verlag, Singapore
EAN: 9789811651595
Print on demand
Delivery on Friday, 14. of August 2026
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Detailed information

This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.
EAN 9789811651595
ISBN 9811651590
Binding Hardback
Publisher Springer Verlag, Singapore
Publication date August 22, 2021
Pages 117
Language English
Dimensions 235 x 155
Country Singapore
Readership Professional & Scholarly
Authors Deshmukh, Jyoti Yogesh; Mahalle Parikshit Narendra; Pise, Priya Dudhale; Shinde, Gitanjali Rahul
Illustrations 50 Illustrations, color; 8 Illustrations, black and white; XIV, 117 p. 58 illus., 50 illus. in color.
Edition 2022 ed.
Series Studies in Big Data
Manufacturer information
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