Practical Data Processing and Analysis Using R

This book is intended as a guide to the analysis and presentation of experimental results. It develops various techniques for the numerical processing of experimental data, using basic statistical met

Tackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to spee

This book covers computationally innovative methods and technologies including data collection and elicitation, data processing, data analysis, data visualizations, and data presentation. It explores

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries

This book covers methods for evaluation of experimental data commonly encountered in science and engineering. Measurements of quantities that vary in a continuous fashion, e.g., the pressure of a gas,

This book discusses both the theory and practical applications of self-correcting data, commonly known as error-correcting codes. The applications included demonstrate the importance of these codes in

Learn how to build an interactive source code analytics system using Roslyn and JavaScript. This concise 150 page book will help you create and use practical code analysis tools utilizing the new feat

This text introduces the use of R statistical software in finance and economics using RStudio. It explains the RStudio interface; common data types and structures used in statistical analysis using R;

As software R&D investment increases, the benefits from short feedback cycles using technologies such as continuous deployment, experimentation-based development, and multidisciplinary teams require a

Written using clear and accessible language, this text provides detailed coverage of the core mathematical concepts underpinning signal processing. All the core areas of mathematics are covered, including generalized inverses, singular value decomposition, function representation, and optimization, with detailed explanations of how basic concepts in these areas underpin the methods used to perform signal processing tasks. A particular emphasis is placed on the practical applications of signal processing, with numerous in-text practice questions and real-world examples illustrating key concepts, and MATLAB programs with accompanying graphical representations providing all the necessary computational background. This is an ideal text for graduate students taking courses in signal processing and mathematical methods, or those who want to establish a firm foundation in these areas before progressing to more advanced study.

This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than G

This practical, conceptual?introduction to statistical analysis by award-winning teacher Andrew N. Christopher uses published?research with inherently interesting social sciences content to help stude

Master the powerful big data analytics capabilities of Oracle R Enterprise? Create and execute dynamic, real-time data analytics using the Oracle in-database capabilities of Oracle R Enterprise. Oracl

This SpringerBrief presents the fundamental concepts of a specialized class of data stream, spatio-temporal data streams, and demonstrates their distributed processing using Big Data frameworks and pl

Think DSP: Digital Signal Processing in Python is an introduction to signal processing and system analysis using a computational approach. The premise of this book (like the others in the Think X seri

More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, you’ll learn best practices for designing a reliable architecture that supp

This book is a “How To” guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includ

This is the first practical book on spatial microsimulation, a multi-level modeling approach that involves the generation, analysis, and modeling of individual-level data allocated to geographical zon

Concise and practical, this is your guide through the methodology and approaches to the analysis of questionnaire data for business and management Masters students.

Concise and practical, this is your guide through the methodology and approaches to the analysis of questionnaire data for business and management Masters students.

This book introduces a new set of orthogonal hybrid functions (HF) which approximates time functions in a piecewise linear manner which is very suitable for practical applications.The book presents an

With an eye towards environmental sustainability, this book presents a step-by-step approach to formulating the resilience of civil infrastructure and energy systems. It provides a concise explanation of resilience terminology, a general overview of theoretical models and analyses, and a clear guide to practical applications, covering critical topics such as interdependent infrastructures and geologic carbon sequestration. Additionally, it contains a general introduction to selected data science topics, so readers can acquire the tools to formulate and analyze resilience engineering problems in depth. Informed by the author's extensive practical experience and thorough academic background, this book includes examples to illustrate key computational algorithms, end-of-chapter exercises and references, and an entire chapter devoted to case studies. Intended for practitioners of resilience engineering with a background in probability, this book offers a balanced blend of background, theor

Real Stats offers an engaging and practical introduction to statistical analysis for upper-level undergraduates and first-year graduate students in political science, public policy, and law. Grounded

This book constitutes the thoroughly refereed proceedings of the Fourth International Symposium on Data-Driven Process Discovery and Analysis held in Riva del Milan, Italy, in November 2014.The five r

R is the world's most popular programming language for data analysis and statistical modeling.The Book of R provides an in-depth, beginner-friendly guide to the R language, and teaches you how to use

This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric Bayesian, and deep learning methods. Details are provided on practical applications in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate motor cortex, or the human EEG, MEG or fMRI, or physiological measurements of heartbeats or blood pressures. With real-world case studies of neuroscience experiments and clinical data sets, and written by expert authors from across the field, this is an ideal resource for anyone working in neuroscience and physiological data analysis.

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problemsAbout This BookUnderstand the principles of Bayesian Inference with less mathematical

This book shows how video technology can be used to inform teachers’ personal practice, and provides new data and real-world case studies not covered by any previous book on the subject.Initial chapte

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLA

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Had

If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is idea

This is an in-depth yet practical guide for Masters students on basic quantitative methods of analysis, covering the foundations through to the analysis.

This is an in-depth yet practical guide for Masters students on basic quantitative methods of analysis, covering the foundations through to the analysis.

A Practical Guide to TPM 2.0: Using the Trusted Platform Module in the New Age of Security is a straight-forward primer for developers. It shows security and TPM concepts, demonstrating their use in r

Medical image analysis using advanced fuzzy set theoretic techniques is an exciting and dynamic branch of image processing. Since the introduction of fuzzy set theory, there has been an explosion of i

This book presents theoretical and practical aspects of the interaction between low and high level image processing. Multiresolution analysis owes its popularity mostly to wavelets and is widely used

R Recipes is your handy problem-solution reference for learning and using the popular R programming language for statistics and other numerical analysis. Packed with hundreds of code and visual recipe

Corruption, Good Governance and Economic Development adopts a non-Eurocentric approach towards good governance issues in Asia and Africa on practical and theoretical levels. Edited by R N Ghosh and M

A practical, proven approach to failure analysis of wood and wood-based products using a full range of forensic analysis methods In Failure Analysis of Wood and Wood-Based Products an expert in the ar