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- Tài liệuOperating Systems and Infrastructure in Data Science(vdf Hochschulverlag, 2023) Spillner, JosefModern data scientists work with a number of tools and operating system facilities in addition to online platforms. Mastering these in combination to manage their data and to deploy software, models and data as ready-to-use online services as well as to perform data science and analysis tasks is in the focus of Operating Systems and Infrastructure in Data Science. Readers will come to understand the fundamental concepts of operating systems and to explore plenty of tools in hands-on tasks and thus gradually develop the skills necessary to compose them for programming in the large, an essential capability in their later career. The book guides students through semester studies, acts as reference knowledge base and aids in acquiring the necessary knowledge, skills and competences especially in self-study settings. A unique feature of the book is the associated access to Edushell, a live environment to practice operating systems and infrastructure tasks.
- Tài liệuDynamic data processing: Recursive least-squares(TU Delft Open, 2024) Teunissen, Peter J.G.This book is a follow-up on Adjustment theory. It extends the theory to the case of time-varying parameters with an emphasis on their recursive determination. Least-squares estimation will be the leading principle used. A least-squares solution is said to be recursive when the method of computation enables sequential, rather than batch, processing of the measurement data. The recursive equations enable the updating of parameter estimates for new observations without the need to store all past observations. Methods of recursive least-squares estimation are therefore particularly useful for applications in which the time-varying parameters need to be instantly determined. Important examples of such applications can be found in the fields of real-time kinematic positioning, navigation and guidance, or multivariate time series analysis. The goal of this book is therefore to convey the necessary knowledge to be able to process sequentially collected measurements for the purpose of estimating time-varying parameters. When determining time-varying parameters from sequentially collected measurement data, one can discriminate between three types of estimation problems: filtering, prediction and smoothing. Filtering aims at the determination of current parameter values, while smoothing and prediction aim at the determination of respectively past and future parameter values. The emphasis in this book will be on recursive least-squares filtering. The theory is worked out for the important case of linear(ized) models. The measurement-update and time-update equations of recursive least-squares are discussed in detail. Models with sequentially collected data, but time-invariant parameters are treated first. In this case only the measurement-update equations apply. State-space models for dynamic systems are discussed so as to include time-varying parameters. This includes their linearization and the construction of the state transition matrix. Elements from the theory of random functions are used to describe the propagation laws for linear dynamic systems. The theory is illustrated by means of many worked out examples. They are drawn from applications such as kinematic positioning, satellite orbit determination and inertial navigation.
- Tài liệuThe DEI Metadata Handbook: A Guide to Diverse, Equitable, and Inclusive Description(Iowa State University Digital Press, 2024) Wintermute, H. E.; Campbell, Heather M.; Dieckman, Christopher S.Written primarily for professionals in library and information science but with applicability to archives and other information management industries, this handbook provides an overview of metadata work that focuses on diversity, equity, and inclusion (DEI). DEI metadata work has several goals: enhancing diverse representation in descriptive metadata; improving discovery of diverse resources; and mitigating negative effects of inaccurate, outdated, or offensive terminology. Readers will gain a broad awareness of DEI-related issues in metadata creation and management; learn techniques for retroactively reviewing and updating existing metadata to address these issues; and develop strategies to create metadata that better meets DEI needs.
- Tài liệuAn Open Guide to Data Structures and Algorithms(PALNI, 2023) Bible, Paul W.; Moser, Lucas; Scarlato, Mia M.This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of speed, memory usage, and programming complexity that separate novice programmers from professional practitioners.
- Tài liệuLinear Regression Using R: An Introduction to Data Modeling(University of Minnesota Libraries Publishing, 2016) Lilja, David J.Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language. All of the necessary resources are freely available online.