Open Source Software for Statistical Analysis of Big Data

ISBN 10: 1799827682 / ISBN 13: 9781799827689

Edité par IGI Global, US, 2020

A propos de cet article

With the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data.Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

N° de réf. du vendeur LU-9781799827689

Détails bibliographiques

• Titre: Open Source Software for Statistical Analysis of Big Data
• Éditeur: IGI Global, US
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 1799827682
• ISBN à 13 chiffres: 9781799827689
• Reliure: Hardback
• Édition: 1st.
• État de l'article: New
• Dimensions: 7 L x 0,63 H x 10 P (en pouces) 7 pouces de largeur par 0,63 pouces de hauteur par 10 pouces de profondeur
• Poids: 633 grammes

A propos de ce titre

Synopsis

With the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

À propos de l?auteur

Dr. Richard S. Segall is a Professor of Computer & Information Technology in the College of Business at Arkansas State University in Jonesboro, AR and also teaches in the Master of Engineering Management (MEM) Program in the College of College of Agriculture, Engineering & Technology. He is also Affiliated Faculty at the University of Arkansas at Little Rock (UALR) where he serves on thesis committees. He holds a Bachelor of Science and Master of Science in Mathematics as well as a Master of Science in Operations Research and Statistics from Rensselaer Polytechnic Institute in Troy, New York. He also holds a PhD in Operations Research form University of Massachusetts at Amherst, He has served on the faculty of Texas Tech University, University of Louisville, University of New Hampshire, University of Massachusetts-Lowell, and West Virginia University. His research interests include data mining, text mining, web mining, database management, Big Data, and mathematical modeling. Dr. Segall's publications have appeared in numerous journals including International Journal of Information Technology and Decision Making (IJITDM), International Journal of Information and Decision Sciences (IJIDS), Applied Mathematical Modelling (AMM), Kybernetes: The International Journal of Cybernetics, Systems and Management Sciences, Journal of the Operational Research Society (JORS) and Journal of Systemics, Cybernetics and Informatics (JSCI) . He has published book chapters in Encyclopedia of Data Warehousing and Mining, Handbook of Computational Intelligence in Manufacturing and Production Management, Handbook of Research on Text and Web Mining Technologies, Encyclopedia of Information Science & Technology, and Encyclopedia of Business Analytics & Optimization . Dr. Segall is a member of the Arkansas Center for Plant-Powered-Production (P3), and on the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM) and International Journal of Data Science (IJDS), and served as Local Arrangements Chair of the MidSouth Computational Biology & Bioinformatics Society (MCBIOS) Conference that was hosted at Arkansas State University. His research has been funded by National Research Council (NRC), U.S. Air Force (USAF), National Aeronautical and Space Administration (NASA), Arkansas Biosciences Institute (ABI), and Arkansas Science & Technology Authority (ASTA). He is recipient of several Session Best Paper awards at World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) conferences. He is co-editor of two other books published by IGI Global: Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications in 2011 and Research and Applications in Global Supercomputing in 2015. Dr. Segall is recipient of Arkansas State University, College of Business Faculty Award for Excellence in Research in 2015.