Foundations of data science for engineering problem solving
Author(s)
Bibliographic Information
Foundations of data science for engineering problem solving
（Studies in big data, v. 94）
Springer, c2022
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Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
Introduction to Data Science.- Data Collection and Preparation.- Data Analysis and Machine learning Algorithms.- Data Visualization Tools and Data Modelling.- Data Science in Information, Communication and Technology.- Data Science in Civil & Mechanical Engineering.- Data Science in Clinical Decision System.- Conclusions.
by "Nielsen BookData"
