Data Science and Big Data in Biology, Physical Science and Engineering: 2nd Edition

Currently, big data analysis represents one of the most important contemporary areas of development and research. 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. The analysis of these enormous amounts of data has become of crucial significance and requires a great deal of effort in order to extract valuable knowledge for decision-making, which, in turn, will make important contributions in both academia and industry.

Big data and data science have emerged 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 enable computers to detect and identify what the data represents and 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, aside from the diverse and varied concepts and techniques relating to big data in biology, chemistry, biomedical engineering, physics, mathematics, and other areas that work with big data.