Statistics And Data Visualization In Climate Science With R And Python
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Statistics And Data Visualization In Climate With R And Python
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Author : Samuel S. Shen
language : en
Publisher:
Release Date : 2023
Statistics And Data Visualization In Climate With R And Python written by Samuel S. Shen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Climatology categories.
"A comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and computing tools for the climate and related sciences. This book is an invaluable reference for students and researchers in climatology and its connected fields who wish to learn data science, statistics, R and Python programming"--
Statistics And Data Visualization In Climate Science With R And Python
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Author : Samuel S. P. Shen
language : en
Publisher: Cambridge University Press
Release Date : 2023-11-30
Statistics And Data Visualization In Climate Science With R And Python written by Samuel S. P. Shen and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-30 with Science categories.
A comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and modern computing tools for the climate sciences and many others such as geography and environmental engineering. It is an invaluable reference for students and researchers in climatology and its connected fields who wish to learn data science, statistics, R and Python programming. The examples and exercises in the book empower readers to work on real climate data from station observations, remote sensing and simulated results. For example, students can use R or Python code to read and plot the global warming data and the global precipitation data in netCDF, csv, txt, or JSON; and compute and interpret empirical orthogonal functions. The book's computer code and real-world data allow readers to fully utilize the modern computing technology and updated datasets. Online supplementary resources include R code and Python code, data files, figure files, tutorials, slides and sample syllabi.
A Hands On Introduction To Data Science
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Author : Chirag Shah
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-02
A Hands On Introduction To Data Science written by Chirag Shah and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Business & Economics categories.
An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.
Climate Mathematics
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Author : Samuel S. P. Shen
language : en
Publisher: Cambridge University Press
Release Date : 2019-09-19
Climate Mathematics written by Samuel S. P. Shen and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-19 with Computers categories.
Presents the core mathematics, statistics, and programming skills needed for modern climate science courses, with online teaching materials.
Python For R Users
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Author : Ajay Ohri
language : en
Publisher: John Wiley & Sons
Release Date : 2017-11-13
Python For R Users written by Ajay Ohri and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-13 with Computers categories.
The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.
Programming With C 20
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Author : Andreas Fertig
language : en
Publisher: Fertig Publications
Release Date : 2021-11-26
Programming With C 20 written by Andreas Fertig and has been published by Fertig Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-26 with Computers categories.
Programming with C++20 teaches programmers with C++ experience the new features of C++20 and how to apply them. It does so by assuming C++11 knowledge. Elements of the standards between C++11 and C++20 will be briefly introduced, if necessary. However, the focus is on teaching the features of C++20. You will start with learning about the so-called big four Concepts, Coroutines, std::ranges, and modules. The big four a followed by smaller yet not less important features. You will learn about std::format, the new way to format a string in C++. In chapter 6, you will learn about a new operator, the so-called spaceship operator, which makes you write less code. You then will look at various improvements of the language, ensuring more consistency and reducing surprises. You will learn how lambdas improved in C++20 and what new elements you can now pass as non-type template parameters. Your next stop is the improvements to the STL. Of course, you will not end this book without learning about what happened in the constexpr-world.
Practical Statistics For Data Scientists
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Author : Peter Bruce
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-05-10
Practical Statistics For Data Scientists written by Peter Bruce and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-10 with Computers categories.
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Effective Data Visualisation For Researchers Principles Tools And Applications
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Author : Daniel Amoah-Oppong
language : en
Publisher: Deep Science Publishing
Release Date : 2025-06-19
Effective Data Visualisation For Researchers Principles Tools And Applications written by Daniel Amoah-Oppong and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-19 with Computers categories.
The growing need for researchers to present their findings in a visually striking and academically rigorous way led to the development of this book. As PhD students in physical education and health promotion at the University of Cape Coast, we have seen firsthand the difficulties many students face in choosing the right visual tools to communicate their data effectively. This book has been written with a deliberate focus on accessibility and practicality. We tried to distil complex principles and technologies of visualisation into simplified, actionable knowledge, suitable for those at the beginning of their academic research careers. While the focus is on academic research, the principles and procedures described in this piece can be extended to professional reports, trade presentations, and policy documents where data-driven reporting is essential. We hope that this book will not only improve the technical skills of the reader but also give them a deeper understanding of the role of visual literacy in the research process. We are grateful to our mentors, colleagues, and students for giving us feedback and support throughout this process. This book is for all researchers and readers who seek truth, meaning, and progress through the written word.
Python And R For The Modern Data Scientist
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Author : Rick J. Scavetta
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-22
Python And R For The Modern Data Scientist written by Rick J. Scavetta and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-22 with Computers categories.
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together
Introduction To Bayesian Statistics
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Author : William M. Bolstad
language : en
Publisher: John Wiley & Sons
Release Date : 2016-09-02
Introduction To Bayesian Statistics written by William M. Bolstad and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-02 with Mathematics categories.
"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.