Data Science : Measuring Uncertainties / editado por Carlos Alberto De Bragança Pereira, Adriano Polpo y Agatha Rodrigues

• TeilnehmendeR: De Bragança Pereira, Carlos Alberto
• Language: English
• Physical Description: recurso en línea (256 p.); il.
• Notes: Este libro es una reimpresión del Special Issue Data Science: Measuring Uncertainties publicadoi previamente en Entropy

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems.