Java Data Object

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

This book provides a significant contribution to the increasing conversation concerning the place of big data in education. Offering a multidisciplinary approach with a diversity of perspectives from international scholars and industry experts, chapter authors engage in both research- and industry-informed discussions and analyses on the place of big data in education, particularly as it pertains to large-scale and ongoing assessment practices moving into the digital space. This volume offers an innovative, practical, and international view of the future of current opportunities and challenges in education and the place of assessment in this context.

Statistics Explained, now in its fully revised Fourth Edition, is for students and researchers who wish to understand the statistical analyses used to analyse quantitative (numerical) research data in

This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; uni

In today’s rapidly evolving digital landscape, safeguarding critical data and systems has never been more vital—or more challenging. Systematic Security: A CISO’s Playbook by Timur Qader offers a groundbreaking guide to building a resilient and scalable security practice from the ground up. Blending real-world experience with practical insights, this book lays out a phased approach to security implementation.Timur begins with describing the current state of security and the landscape security professionals find themselves in that offer both opportunity and risk. He goes on to systematically address strategic design, operational efficiency, risk-based modeling, and a rapidly growing regulatory landscape. This essential resource provides step-by-step strategies for implementing a comprehensive security framework. Whether you''re launching a new security program or transforming an existing one, this book delivers actionable insights on governance, compliance, and advanced security operati

Sixty years after its birth, Synthetic Aperture Radar (SAR) evolved as a key player of earth observation, and it is continually upgraded by enhanced hardware functionality and improved overall performance in response to user requirements. The basic information gained by SAR includes the backscattering coefficient of targets, their phases (the truncated distance between SAR and its targets), and their polarization dependence. The spatiotemporal combination of the multiple data operated on the satellite or aircraft significantly increases its sensitivity to detect changes on earth, including temporal variations of the planet in amplitude and the interferometric change for monitoring disasters; deformations caused by earthquakes, volcanic activity, and landslides; environmental changes; ship detection; and so on.Earth-orbiting satellites with the appropriate sensors can detect environmental changes because of their large spatial coverage and availability. Imaging from Spaceborne and Airbo