Scientific Data Analysis

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A Practical Guide to Scientific Data Analysis

David J. Livingstone
A Practical Guide to Scientific Data Analysis Book PDF

Author : David J. Livingstone
Publisher : John Wiley & Sons
Page : 358 pages
File Size : 49,5 Mb
Release : 2009-12-10
Category : Science
ISBN : 9780470684818

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A Practical Guide to Scientific Data Analysis by David J. Livingstone Pdf

Inspired by the author's need for practical guidance in the processes of data analysis, A Practical Guide to Scientific Data Analysis has been written as a statistical companion for the working scientist. This handbook of data analysis with worked examples focuses on the application of mathematical and statistical techniques and the interpretation of their results. Covering the most common statistical methods for examining and exploring relationships in data, the text includes extensive examples from a variety of scientific disciplines. The chapters are organised logically, from planning an experiment, through examining and displaying the data, to constructing quantitative models. Each chapter is intended to stand alone so that casual users can refer to the section that is most appropriate to their problem. Written by a highly qualified and internationally respected author this text: Presents statistics for the non-statistician Explains a variety of methods to extract information from data Describes the application of statistical methods to the design of “performance chemicals” Emphasises the application of statistical techniques and the interpretation of their results Of practical use to chemists, biochemists, pharmacists, biologists and researchers from many other scientific disciplines in both industry and academia.

Statistics and Analysis of Scientific Data

Massimiliano Bonamente
Statistics and Analysis of Scientific Data Book PDF

Author : Massimiliano Bonamente
Publisher : Springer
Page : 318 pages
File Size : 42,9 Mb
Release : 2016-11-08
Category : Science
ISBN : 9781493965724

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Statistics and Analysis of Scientific Data by Massimiliano Bonamente Pdf

The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: • a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. • a new chapter on the various measures of the mean including logarithmic averages. • new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. • a new case study and additional worked examples. • mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text. • end-of-chapter summary boxes, for easy reference. As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.

Introduction to Data Science

Rafael A. Irizarry
Introduction to Data Science Book PDF

Author : Rafael A. Irizarry
Publisher : CRC Press
Page : 794 pages
File Size : 40,8 Mb
Release : 2019-11-20
Category : Mathematics
ISBN : 9781000708035

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Introduction to Data Science by Rafael A. Irizarry Pdf

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

R for Data Science

Hadley Wickham,Garrett Grolemund
R for Data Science Book PDF

Author : Hadley Wickham,Garrett Grolemund
Publisher : "O'Reilly Media, Inc."
Page : 521 pages
File Size : 52,8 Mb
Release : 2016-12-12
Category : Computers
ISBN : 9781491910368

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R for Data Science by Hadley Wickham,Garrett Grolemund Pdf

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Scientific Data Analysis

Graham Currell
Scientific Data Analysis Book PDF

Author : Graham Currell
Publisher : Oxford University Press, USA
Page : 353 pages
File Size : 52,9 Mb
Release : 2015
Category : Science
ISBN : 9780198712541

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Scientific Data Analysis by Graham Currell Pdf

Drawing on the author's extensive experience of supporting students undertaking projects, 'Scientific Data Analysis' is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way.

Advanced Excel for Scientific Data Analysis

Robert De Levie
Advanced Excel for Scientific Data Analysis Book PDF

Author : Robert De Levie
Publisher : Oxford University Press, USA
Page : 631 pages
File Size : 48,5 Mb
Release : 2004
Category : Computers
ISBN : 9780195152753

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Advanced Excel for Scientific Data Analysis by Robert De Levie Pdf

This guide to Excel focuses on three areas--least squares, Fourier transformation, and digital simulation. It illustrates the techniques with detailed examples, many drawn from the scientific literature. It also includes and describes a number of sample macros and functions to facilitate common data analysis tasks. De Levie is affiliated with Bowdoin College. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com).

Scientific Data Analysis

Richard L. Jr. Branham
Scientific Data Analysis Book PDF

Author : Richard L. Jr. Branham
Publisher : Springer Science & Business Media
Page : 246 pages
File Size : 46,8 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461233626

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Scientific Data Analysis by Richard L. Jr. Branham Pdf

This monograph is concerned with overdetermined systems, inconsistent systems with more equations than unknowns, in scientific data reduction. It is not a text on statistics, numerical methods, or matrix cOmputations, although elements of all three, especially the latter, enter into the discussion. The reader I have in mind is a scientist or engineer who has gathered data that he or she wants to model by a mathematical system, perhaps linear, perhaps nonlinear, and solve to obtain the best estimates, in some sense of the term "best," of various parameters. Because the calculations will be performed on a digital computer, the first chapter discusses floating-point numbers and their effect on mathematical operations. The chapter ends with some methods for accurately summing floating-point numbers, an operation frequently required in numerical work and one often done by the worst possible method, recursive summation. Chapter 2 gives a brief review of linear algebra and includes vector and matrix norms and condition numbers of matrices and linear systems. ' Chapter 3 presents some ideas for manipulating sparse matrices. Frequently, time or memory can be saved by use of sparse matrix techniques. The subject is extensive and the chapter is only indicative of the many techniques available. Although Chapter 3 is somewhat extraneous to the rest of the book, Chapter 5, on linear least squares, makes use of the compressed storage mode for the symmetric matrices discussed in Chapter 3.

Data Analysis for Social Science

Elena Llaudet,Kosuke Imai
Data Analysis for Social Science Book PDF

Author : Elena Llaudet,Kosuke Imai
Publisher : Princeton University Press
Page : 256 pages
File Size : 51,6 Mb
Release : 2022-11-29
Category : Computers
ISBN : 9780691199436

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Data Analysis for Social Science by Elena Llaudet,Kosuke Imai Pdf

"Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors"--

Data Analysis Techniques for Physical Scientists

Claude A. Pruneau
Data Analysis Techniques for Physical Scientists Book PDF

Author : Claude A. Pruneau
Publisher : Cambridge University Press
Page : 719 pages
File Size : 46,8 Mb
Release : 2017-10-05
Category : Language Arts & Disciplines
ISBN : 9781108416788

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Data Analysis Techniques for Physical Scientists by Claude A. Pruneau Pdf

A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.

Entertainment Science

Thorsten Hennig-Thurau,Mark B. Houston
Entertainment Science Book PDF

Author : Thorsten Hennig-Thurau,Mark B. Houston
Publisher : Springer
Page : 865 pages
File Size : 47,7 Mb
Release : 2018-08-01
Category : Business & Economics
ISBN : 9783319892924

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Entertainment Science by Thorsten Hennig-Thurau,Mark B. Houston Pdf

The entertainment industry has long been dominated by legendary screenwriter William Goldman’s “Nobody-Knows-Anything” mantra, which argues that success is the result of managerial intuition and instinct. This book builds the case that combining such intuition with data analytics and rigorous scholarly knowledge provides a source of sustainable competitive advantage – the same recipe for success that is behind the rise of firms such as Netflix and Spotify, but has also fueled Disney’s recent success. Unlocking a large repertoire of scientific studies by business scholars and entertainment economists, the authors identify essential factors, mechanisms, and methods that help a new entertainment product succeed. The book thus offers a timely alternative to “Nobody-Knows” decision-making in the digital era: while coupling a good idea with smart data analytics and entertainment theory cannot guarantee a hit, it systematically and substantially increases the probability of success in the entertainment industry. Entertainment Science is poised to inspire fresh new thinking among managers, students of entertainment, and scholars alike. Thorsten Hennig-Thurau and Mark B. Houston – two of our finest scholars in the area of entertainment marketing – have produced a definitive research-based compendium that cuts across various branches of the arts to explain the phenomena that provide consumption experiences to capture the hearts and minds of audiences. Morris B. Holbrook, W. T. Dillard Professor Emeritus of Marketing, Columbia University Entertainment Science is a must-read for everyone working in the entertainment industry today, where the impact of digital and the use of big data can’t be ignored anymore. Hennig-Thurau and Houston are the scientific frontrunners of knowledge that the industry urgently needs. Michael Kölmel, media entrepreneur and Honorary Professor of Media Economics at University of Leipzig Entertainment Science’s winning combination of creativity, theory, and data analytics offers managers in the creative industries and beyond a novel, compelling, and comprehensive approach to support their decision-making. This ground-breaking book marks the dawn of a new Golden Age of fruitful conversation between entertainment scholars, managers, and artists. Allègre Hadida, Associate Professor in Strategy, University of Cambridge

Special Topics in Information Technology

Luigi Piroddi
Special Topics in Information Technology Book PDF

Author : Luigi Piroddi
Publisher : Springer Nature
Page : 151 pages
File Size : 48,5 Mb
Release : 2022-01-01
Category : Technology & Engineering
ISBN : 9783030859183

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Special Topics in Information Technology by Luigi Piroddi Pdf

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.

Introduction to Scientific Computing and Data Analysis

Mark H. Holmes
Introduction to Scientific Computing and Data Analysis Book PDF

Author : Mark H. Holmes
Publisher : Springer Nature
Page : 563 pages
File Size : 40,5 Mb
Release : 2023-07-11
Category : Computers
ISBN : 9783031224300

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Introduction to Scientific Computing and Data Analysis by Mark H. Holmes Pdf

This textbook provides an introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression-based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The codes used for most of the computational examples in the text are available on GitHub. This new edition includes material necessary for an upper division course in computational linear algebra.

R for Health Data Science

Ewen Harrison,Riinu Pius
R for Health Data Science Book PDF

Author : Ewen Harrison,Riinu Pius
Publisher : CRC Press
Page : 354 pages
File Size : 41,7 Mb
Release : 2020-12-31
Category : Medical
ISBN : 9781000226164

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R for Health Data Science by Ewen Harrison,Riinu Pius Pdf

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms.

Introduction to Python in Earth Science Data Analysis

Maurizio Petrelli
Introduction to Python in Earth Science Data Analysis Book PDF

Author : Maurizio Petrelli
Publisher : Springer Nature
Page : 229 pages
File Size : 45,5 Mb
Release : 2021-09-16
Category : Science
ISBN : 9783030780555

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Introduction to Python in Earth Science Data Analysis by Maurizio Petrelli Pdf

This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.

Topological Data Analysis for Scientific Visualization

Julien Tierny
Topological Data Analysis for Scientific Visualization Book PDF

Author : Julien Tierny
Publisher : Springer
Page : 150 pages
File Size : 49,9 Mb
Release : 2018-01-16
Category : Mathematics
ISBN : 9783319715070

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Topological Data Analysis for Scientific Visualization by Julien Tierny Pdf

Combining theoretical and practical aspects of topology, this book provides a comprehensive and self-contained introduction to topological methods for the analysis and visualization of scientific data. Theoretical concepts are presented in a painstaking but intuitive manner, with numerous high-quality color illustrations. Key algorithms for the computation and simplification of topological data representations are described in detail, and their application is carefully demonstrated in a chapter dedicated to concrete use cases. With its fine balance between theory and practice, "Topological Data Analysis for Scientific Visualization" constitutes an appealing introduction to the increasingly important topic of topological data analysis for lecturers, students and researchers.