This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.
The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The concise mathematical treatment of the subject matter is illustrated by many examples and for the present edition a library of Java programs has been developed. It comprises methods of numerical data analysis and graphical representation as well as many example programs and solutions to programming problems. The book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, in working for bachelor or master degrees, in thesis work, and in research and professional work.
Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. Advanced computational and statistical methodologies for analysing big data are developed Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable Case studies are discussed to demonstrate the implementation of the developed methods Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation Computing code/programs are provided where appropriate
Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen.
The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.
This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.
Programming; Data management and manipulation; Numerical computations; Linear models; Nonlinear models; Simulation of Random processes; Computational graphics.
This book is designed to equip students to navigate through MS Excel and basic data computation methods, which are essential tools in research or professional settings and in classrooms. It illustrates the concepts used in research and data analysis and economic and financial decision-making in business and in daily life. The book will help students acquire knowledge and develop skills in statistical techniques and financial analysis using MS Excel. With illustrations and examples, it will help the readers to: Visualize, present, and analyze data through MS Excel spreadsheets and tables and create personal or business spreadsheets Learn how to work with spreadsheets, use formulae, and perform calculations and analysis Create tables including Pivot Tables Become familiar with basic statistical and financial measures Design creative spread sheets and communicate effectively in business using spreadsheets and data analysis This revised and updated second edition will be an essential resource for students of economics, commerce, management, and other social science subjects, and will be useful to those studying econometrics, financial technology, basic computational techniques, data analysis, and applied economics. Content The book is developed through three phases, with each phase standing on its own as well as providing a foundation for the next. In the first phase, Excel is introduced for the students to learn entry of data, manipulation of data, carrying out operations and develop presentations. The second phase introduces basic statistical measures of data summarisation and analysis, following which these are illustrated in Excel spreadsheets with the techniques introduced in the first phase. In addition, a few advanced tools of statistical analysis are introduced and illustrated in Excel. The third phase introduces financial measures of common use, their general computation and working them out in Excel. The book intends to illustrate the concepts used in economic and financial decision-making in business and in daily life; it helps demonstrate a deeper understanding from both theoretical and practical perspectives. An effort has been made to make the book student-friendly by using simple language and giving a number of illustrations in each chapter, solved in such a simple manner that they can be easily understood by the students. Practical questions have been included at the end of each chapter so that the students can independently solve them and test their understanding of the concepts and computations introduced in the chapter. Outcome At the end, students will be able to describe what a spreadsheet is and what Excel’s capabilities are and can work with elements that make up the structure of a worksheet. They will be able to work with spreadsheets and enter data in Excel, use formulae and calculations, and create tables, charts and pivot tables. They will be familiar with basic statistical and financial measures of general use. They will be able to do basic computations in statistics and finance in Excel. Students will acquire the capacity to create personal and/or business spreadsheets following current professional and/or industry standards. Their potential for critical thinking to design and create spreadsheets and communicate in a business setting using spreadsheet vocabulary will be enhanced. In the digital age, students necessarily need to know data, data sources and how to ‘dirty’ their hands with data. There can be no substitute to ‘talking through numbers’. The book introduces students to a variety of Indian and International data sources and teaches them how to import data-be it social, economic, financial and so on-to the Excel sheet. Once they master it, the data world is there for them to conquer! The educational background required for the student to understand the text is some basic English and Mathematics of school-leaving level. Some fl air for numbers will be an asset and for them it will be a breeze; others will have to make an effort but ample illustrations and practice questions make life simple, whether it is basic statistics or slightly intricate finance!
Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra, interpolation, numerical integration, optimization, and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R, along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R, but are interested in learning more about how the underlying algorithms work. As such, it is suitable for statisticians, economists, and engineers, and others with a computational and numerical background.
