Data Science and Big Data in Biology, Physical Science and Engineering
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Big Data analysis is one of the most contemporary areas of development and research in the present day. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. Analysis of these enormous amounts of data has become a crucial need and requires a lot of effort in order to extract valuable knowledge for decision-making, which in turn will help both academia and industry. Big Data and Data Science have appeared due to the significant need for generating, storing, organizing, and...
Big Data analysis is one of the most contemporary areas of development and research in the present day. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. Analysis of these enormous amounts of data has become a crucial need and requires a lot of effort in order to extract valuable knowledge for decision-making, which in turn will help both academia and industry. Big Data and Data Science have appeared due to the significant need for generating, storing, organizing, and processing immense amounts of data. Data scientists strive to use Artificial Intelligence (AI) and Machine Learning (ML) approaches and models to allow computers to detect and identify what the data represents and be able to detect patterns more quickly, efficiently, and reliably than humans. The goal behind this Special Issue is to explore and discuss various principles, tools, and models in the context of Data Science, as well as diverse and varied concepts and techniques in Big Data in Biology, Chemistry, Biomedical Engineering, Physics, Mathematics, and other areas that work with Big Data.
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