Der Spion in meiner Tasche

DATA SCIENCE IN THEORY AND PRACTICE[/b] EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

Este libro es una gu?a de campo que puede ser usada por el p?blico en general para el estudio de los odonatos asociados a ambientes de este departamento. Cuenta con m?s de 150 fotograf?as e ilustraciones que muestran detalles de comportamiento, ecolog?a y morfolog?a de casi un centenar de especies. Adicionalmente, se presenta informaci?n para determinar el nivel de familia odonata a trav?s de una clave taxon?mica did?ctica. Para cada especie se provee informaci?n para su identificaci?n, comportamiento, h?bitat y categorizaci?n seg?n la International Union for Conservation of Nature (IUCN). Finalmente, se plantean algunos aspectos b?sicos para su conservaci?n.

Bruno Baur erl?utert grundlegende Aspekte und Prozesse, die zu Ver?nderungen in der Vielfalt des Lebens auf unserem Planeten f?hren. Auf faszinierende Weise werden Fakten, Wertungen und Forderungen im Umgang mit der Vielfalt des Lebens auf unserem Planeten vorgestellt. Dabei wird gezeigt, dass Biodiversit?t nicht blo? ein Z?hlen und Registrieren von Arten ist. Vielmehr wird der Nutzen und die Nutzung der Biodiversit?t aus multidisziplin?rer Sicht erl?utert.

An accessible and engaging introduction to the study of probability and statistics Utilizing entertaining real-world examples, A Probability and Statistics Companion provides aunique, interesting, and accessible introduction to probability and statistics. This one-of-a-kind book delves into practical topics that are crucial in the analysis of sample surveys and experimentation. This handy book contains introductory explanations of the major topics in probability and statistics, including hypothesis testing and regression, while also delving into more advanced topics such as the analysis of sample surveys, analysis of experimental data, and statistical process control. The book recognizes that there are many sampling techniques that can actually improve on simple random sampling, and in addition, an introduction to the design of experiments is provided to reflect recent advances in conducting scientific experiments. This blend of coverage results in the development of a deeper understanding and solid foundation for the study of probability and statistics. Additional topical coverage includes: Probability and sample spaces Choosing the best candidate Acceptance sampling Conditional probability Random variables and discrete probability distributions Waiting time problems Continuous probability distributions Statistical inference Nonparametric methods Least squares and medians Recursions and probability Each chapter contains exercises and explorations for readers who wish to conduct independent projects or investigations. The discussion of most methods is complemented with applications to engaging, real-world scenarios such as winning speeds at the Indianapolis 500 and predicting winners of the World Series. In addition, the book enhances the visual nature of the subject with numerous multidimensional graphical representations of the presented examples. A Probability and Statistics Companion is an excellent book for introductory probability and statistics courses at the undergraduate level. It is also a valuable reference for professionals who use statistical concepts to make informed decisions in their day-to-day work.

The first part of the book defines the concept of uncertainties and the mathematical frameworks that will be used for uncertainty modeling. The application to system reliability assessment illustrates the concept. In the second part, evidential networks as a new tool to model uncertainty in reliability and risk analysis is proposed and described. Then it is applied on SIS performance assessment and in risk analysis of a heat sink. In the third part, Bayesian and evidential networks are used to deal with important measures evaluation in the context of uncertainties.