TY - BOOK ID - 103616206 TI - Data Science in Societal Applications : Concepts and Implications AU - Nguyen, Nhu Gia AU - Pandey, Manjusha AU - Rautaray, Siddharth Swarup PY - 2022 SN - 9811951535 9811951543 PB - Singapore : Springer Nature Singapore : Imprint: Springer, DB - UniCat KW - Big data.. KW - Data mining.. KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Data sets, Large KW - Large data sets KW - Data sets KW - Artificial intelligence KW - Application software. KW - Big data. KW - Quantitative research. KW - Data Science. KW - Computer and Information Systems Applications. KW - Big Data. KW - Data Analysis and Big Data. KW - Data processing. KW - Data analysis (Quantitative research) KW - Exploratory data analysis (Quantitative research) KW - Quantitative analysis (Research) KW - Quantitative methods (Research) KW - Research KW - Application computer programs KW - Application computer software KW - Applications software KW - Apps (Computer software) KW - Computer software UR - https://www.unicat.be/uniCat?func=search&query=sysid:103616206 AB - The book provides an insight into the practical applications and theoretical foundation of data science. The book discusses new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The book includes contributions from academia and industry experts detailing the shortfalls of current tools and techniques used and generating the blueprint of the new technologies. The topics covered in the book range from theoretical and foundational research, platforms, methods, applications, and tools in data science. The chapters in the book add a social, geographical, and temporal dimension to data science research. The papers included are application-oriented that prepare and use data in discovery research. This book will provide researchers and practitioners with a detailed snapshot of current progress in data science. Moreover, it will stimulate new study, research, and the development of new applications. . ER -