Inhaltsangabe:
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.
Über die Autorinnen und Autoren:
Dr. Richard S. Segall is Professor of Information Systems and Business Analytics in Neil Griffin College of Business at Arkansas State University in Jonesboro, AR where he also taught for ten years in the College of Engineering & Computer Science Master of Engineering Management (MEM) Program and is Affiliated Faculty of the Environmental Sciences Program and Center for No-Boundary Thinking (CNBT). He is also an Affiliated Faculty at the University of Arkansas at Little Rock (UALR) where he serves on thesis committees. His research interests include data mining, text mining, web mining, database management, Big Data, and mathematical modeling. 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). His publications have appeared in IGI Global journals of: International Journal of Fog Computing (IJFC), International Journal of Open Source Software and Processes (IJOSP), and International Journal of Big Data and Analytics in Healthcare (IJBDAH). He is also a member of the Editorial Review Board for the International Journal of Fog Computing (IJFC).
Gao Niu is an Assistant Professor in Actuarial Science and Program Coordinator of Actuarial Math Program at Bryant University. He also serves as the Faculty Consultant of the Janet & Mark L Goldenson Center for Actuarial Research at the University of Connecticut. He has a doctorate in actuarial science from the University of Connecticut, is an Associate of the Casualty Actuarial Society and a Member of the American Academy of Actuaries. Dr. Niu has years of experience in academic actuarial research and consulting practice. His research area includes but not limited to the following: big data analytics application in insurance industry, property and casualty insurance practice, predictive modeling, agent-based modeling, financial planning, life insurance and health insurance pricing, reserving and data mining.
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