This volume is devoted to the most recent discoveries in mathematics and statistics. It also serves as a platform for knowledge and information exchange between experts from industrial and academic sectors. The book covers a wide range of topics, including mathematical analyses, probability, statistics, algebra, geometry, mathematical physics, wave propagation, stochastic processes, ordinary and partial differential equations, boundary value problems, linear operators, cybernetics and number and functional theory. It is a valuable resource for pure and applied mathematicians, statisticians, engineers and scientists.

This is an open access book. The ICMSS2022 is an international conference jointly organised by the Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia together with the Banasthali University, Jaipur, India. This international conference aims to give exposure and to bring together academicians, researchers and industry experts for intellectual growth. The ICMSS2022 serves as a platform for the scientific community members to exchange ideas and approaches, to present research findings, and to discuss current issues and topics related to mathematics, statistics as well as their applications. Objectives: to present the most recent discoveries in mathematics and statistics. to serve as a platform for knowledge and information sharing between experts from industries and academia. to identify and create potential collaboration among participants. The organising committee of ICMSS2022 welcomes all delegates to deliberate over various aspects related to the conference themes and sub-themes.

These proceedings gather outstanding research papers presented at the Second International Conference on Data Engineering 2015 (DaEng-2015) and offer a consolidated overview of the latest developments in databases, information retrieval, data mining and knowledge management. The conference brought together researchers and practitioners from academia and industry to address key challenges in these fields, discuss advanced data engineering concepts and form new collaborations. The topics covered include but are not limited to: • Data engineering • Big data • Data and knowledge visualization • Data management • Data mining and warehousing • Data privacy & security • Database theory • Heterogeneous databases • Knowledge discovery in databases • Mobile, grid and cloud computing • Knowledge management • Parallel and distributed data • Temporal data • Web data, services and information engineering • Decision support systems • E-Business engineering and management • E-commerce and e-learning • Geographical information systems • Information management • Information quality and strategy • Information retrieval, integration and visualization • Information security • Information systems and technologies

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

This handbook connects the practice of statistics to the teaching and learning of the subject with contributions from experts in several disciplines. Chapters present current challenges and methods of statistics education in the changing world for statistics and mathematics educators. Issues addressed include current and future challenges in professional development of teachers, use of technology tools, design of learning environments and appropriate student assessments. This handbook presents challenging and inspiring international research perspectives on the history and nature, current issues, and future directions of statistics education and statistics education research.

This book is a printed edition of the Special Issue "Differential Geometrical Theory of Statistics" that was published in Entropy

Questions about the reproducibility of scientific research have been raised in numerous settings and have gained visibility through several high-profile journal and popular press articles. Quantitative issues contributing to reproducibility challenges have been considered (including improper data measurement and analysis, inadequate statistical expertise, and incomplete data, among others), but there is no clear consensus on how best to approach or to minimize these problems. A lack of reproducibility of scientific results has created some distrust in scientific findings among the general public, scientists, funding agencies, and industries. While studies fail for a variety of reasons, many factors contribute to the lack of perfect reproducibility, including insufficient training in experimental design, misaligned incentives for publication and the implications for university tenure, intentional manipulation, poor data management and analysis, and inadequate instances of statistical inference. The workshop summarized in this report was designed not to address the social and experimental challenges but instead to focus on the latter issues of improper data management and analysis, inadequate statistical expertise, incomplete data, and difficulties applying sound statistic inference to the available data. Many efforts have emerged over recent years to draw attention to and improve reproducibility of scientific work. This report uniquely focuses on the statistical perspective of three issues: the extent of reproducibility, the causes of reproducibility failures, and the potential remedies for these failures.

Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery. Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.