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Geographic data science with R :, Wimberly, Michael C.,


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Цена: 12554.00р.
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При оформлении заказа до: 2025-08-18
Ориентировочная дата поставки: конец Сентября - начало Октября
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Автор: Wimberly, Michael C.,
Название:  Geographic data science with R :
ISBN: 9781032347714
Издательство: Taylor&Francis
Классификация:



ISBN-10: 1032347716
Обложка/Формат: Hardback
Страницы: 306
Вес: 0.54 кг.
Дата издания: 08.05.2023
Серия: Chapman & hall/crc data science series
Язык: Portuguese
Иллюстрации: 4 tables, black and white; 56 line drawings, color; 46 line drawings, black and white; 22 halftones, color; 1 halftones, black and white; 78 illustrations, color; 47 illustrations, black and white
Размер: 162 x 241 x 20
Читательская аудитория: Professional & vocational
Подзаголовок: Visualizing and analyzing environmental change
Ссылка на Издательство: Link
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Поставляется из: Европейский союз
Описание: There is a lack of books on the broader topic of scientific workflows for geospatial data processing and analysis. This book aims to fill this gap by providing a series of tutorials aimed at teaching good practices for using geospatial data to address problems in environmental geography.


The Elements of Statistical Learning

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 10480.00 р.
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Scientific Data Analysis

Автор: Currell Graham
Название: Scientific Data Analysis
ISBN: 0198712545 ISBN-13(EAN): 9780198712541
Издательство: Oxford Academ
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Цена: 7602.00 р.
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Описание: 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.

Excel data analysis

Автор: Guerrero, Hector
Название: Excel data analysis
ISBN: 3030012786 ISBN-13(EAN): 9783030012786
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This book offers a comprehensive and readable introduction to modern business and data analytics.

Data science

Автор: Timbers, Tiffany-anne (university Of British Colum
Название: Data science
ISBN: 0367524686 ISBN-13(EAN): 9780367524685
Издательство: Taylor&Francis
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Цена: 7501.00 р.
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Описание: Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.

Metrics That Make a Difference: How to Analyze Change and Error

Автор: Pontius Jr Robert Gilmore
Название: Metrics That Make a Difference: How to Analyze Change and Error
ISBN: 3030707644 ISBN-13(EAN): 9783030707644
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This book gives you insights necessary to interpret metrics that make a difference in life`s decisions.This book gives methods and software that are essential to analyze change and error.

The Analysis of Biological Data

Автор: Michael C. Whitlock, Dolph Schluter
Название: The Analysis of Biological Data
ISBN: 1319325343 ISBN-13(EAN): 9781319325343
Издательство: Macmillan Learning
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Цена: 13858.00 р.
Наличие на складе: Поставка под заказ.

Описание: The evolution of a classicThe new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level.

Econometric Analysis of Panel Data

Автор: Baltagi Badi H.
Название: Econometric Analysis of Panel Data
ISBN: 3030539520 ISBN-13(EAN): 9783030539528
Издательство: Springer
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Цена: 9083.00 р.
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Описание: Introduction.- The One-Way Error Component Regression Model.- The Two-Way Error Component Regression Model.- Test of Hypotheses with Panel Data.- Heteroskedasticity and Serial Correlation in the Error Component Model.- Seemingly Unrelated Regressions with Error Components.- Simultaneous Equations with Error Components.- Dynamic Panel Data Models.- Unbalanced Panel Data Models.- Special Topics.- Limited Dependent Variables and Panel Data.- Nonstationary Panels.- Spatial Panel Data Models.

Bad Data: Why We Measure the Wrong Things and Often Miss the Metrics That Matter

Автор: Schryvers Peter
Название: Bad Data: Why We Measure the Wrong Things and Often Miss the Metrics That Matter
ISBN: 1633885909 ISBN-13(EAN): 9781633885905
Издательство: NBN International
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Цена: 4011.00 р.
Наличие на складе: Нет в наличии.

Описание: Highlights the pitfalls of data analysis and emphasizes the importance of using the appropriate metrics before making key decisions. Big data is often touted as the key to understanding almost every aspect of contemporary life. This critique of "information hubris" shows that even more important than data is finding the right metrics to evaluate it. The author, an expert in environmental design and city planning, examines the many ways in which we measure ourselves and our world. He dissects the metrics we apply to health, worker productivity, our children's education, the quality of our environment, the effectiveness of leaders, the dynamics of the economy, and the overall well-being of the planet. Among the areas where the wrong metrics have led to poor outcomes, he cites the fee-for-service model of health care, corporate cultures that emphasize time spent on the job while overlooking key productivity measures, overreliance on standardized testing in education to the detriment of authentic learning, and a blinkered focus on carbon emissions, which underestimates the impact of industrial damage to our natural world. He also examines various communities and systems that have achieved better outcomes by adjusting the ways in which they measure data. The best results are attained by those that have learned not only what to measure and how to measure it, but what it all means. By highlighting the pitfalls inherent in data analysis, this illuminating book reminds us that not everything that can be counted really counts.

Econometrics in practice /

Автор: Turner, Paul,
Название: Econometrics in practice /
ISBN: 1683926609 ISBN-13(EAN): 9781683926603
Издательство: Mare Nostrum (Eurospan)
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Цена: 10395.00 р.
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Описание: Covers the econometric methods necessary for a practicing applied economist or data analyst. This requires both an understanding of statistical theory and how it is used in actual applications. Chapters 1 to 9 present the material concerned with basic statistical theory. Chapters 10 to 13 introduce a number of more advanced topics.

Data science and innovations for intelligent systems :

Автор: Kavita Taneja, Harmunish Taneja
Название: Data science and innovations for intelligent systems :
ISBN: 0367676273 ISBN-13(EAN): 9780367676278
Издательство: Taylor&Francis
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Цена: 22202.00 р.
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Описание: This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems.

Geographic Data Science with Python

Автор: Rey, Sergio
Название: Geographic Data Science with Python
ISBN: 1032445955 ISBN-13(EAN): 9781032445953
Издательство: Taylor&Francis
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Цена: 7654.00 р.
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Random Fields for Spatial Data Modeling

Автор: Dionisis Hristopulos
Название: Random Fields for Spatial Data Modeling
ISBN: 9402419160 ISBN-13(EAN): 9789402419160
Издательство: Springer
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Цена: 16769.00 р.
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Описание: Introduction.- Preliminary Remarks.- Why Random Fields?.- Notation and Definitions.- Noise and Errors.- Spatial Data and Basic Processing Procedures.- A Personal Selection of Relevant Books.- Trend Models and Estimation.- Empirical Trend Estimation.- Regression Analysis.- Global Trend Models.- Local Trend Models.- Trend Estimation based on Physical Information.- Trend Based on the Laplace Equation.- Basic Notions of Random Fields.- Introduction.- Single-Point Description.- Stationarity and Statistical Homogeneity.- Variogram versus Covariance.- Permissibility of Covariance Functions.- Permissibility of Variogram Functions.- Additional Topics of Random Field Modeling.- Ergodicity.- Statistical Isotropy.- Anisotropy.- Anisotropic Spectral Densities.- Multipoint Description of Random Fields.- Geometric Properties of Random Fields.- Local Properties.- Covariance Hessian Identity and Geometric Anisotropy.- Spectral Moments.- Length Scales of Random Fields.- Fractal Dimension.- Long-Range Dependence.- Intrinsic Random Fields.- Fractional Brownian Motion.- Classification of Random Fields.- Gaussian Random Fields.- Multivariate Normal Distribution.- Field Integral Formulation.- Useful Properties of Gaussian Random Fields.- Perturbation Theory for Non-Gaussian Probability Densities.- Non-stationary Covariance Functions.- Further Reading.- Random Fields based on Local Interactions.- Spartan Spatial Random Fields.- Two-point Functions and Realizations.- Statistical and Geometric Properties.- Bessel-Lommel Covariance Functions.- Lattice Representations of Spartan Random Fields.- Introduction to Gauss-Markov Random Fields.- From Spartan Random Fields to Gauss-Markov Random Fields.- Lattice Spectral Density.- SSRF Lattice Moments.- SSRF Inverse Covariance Operator on Lattices.- Spartan Random Fields and Langevin Equations.- Introduction to Stochastic Differential Equations.- Classical Harmonic Oscillator.- Stochastic Partial Differential Equations.- Spartan Random Fields and Stochastic Partial Differential Equations.- Covariance and Green's functions.- Whittle-Matйrn Stochastic Partial Differential Equation.- Diversion in Time Series.- Spatial Prediction Fundamentals.- General Principles of Linear Prediction.- Deterministic Interpolation.- Stochastic Methods.- Simple Kriging.- Ordinary Kriging.- Properties of the Kriging Predictor.- Topics Related to the Application of Kriging.- Evaluating Model Performance.- More on Spatial Prediction.- Linear Generalizations of Kriging.- Nonlinear Extensions of Kriging.- Connections with Gaussian Process Regression.- Bayesian Kriging.- Continuum Formulation of Linear Prediction.- The "Local-Interaction" Approach.- Big Spatial Data.- Basic Concepts and Methods of Estimation.- Estimator Properties.- Estimating the Mean with Ordinary Kriging.- Variogram Estimation.- Maximum Likelihood Estimation.- Cross Validation.- More on Estimation.- The Method of Normalized Correlations.- The Method of Maximum Entropy.- Stochastic Local Interactions.- Measuring Ergodicity.- Beyond the Gaussian Models.- Trans-Gaussian Random Fields.- Gaussian Anamorphosis.- Tukey g-h Random Fields.- Transformations based on Kappa Exponentials.- Hermite Polynomials.- Multivariate Student's t-distribution.- Copula Models.- The Replica Method.- Binary Random Fields.- The Indicator Random Field.- Ising Model.- Generalized Linear Models.- Simulations.- Introduction.- Covariance Matrix Factorization.- Spectral Simulation Methods.- Fast-Fourier-Transform Simulation.- Randomized Spectral Sampling.- Conditional Simulation based on Polarization Method.- Conditional Simulation based on Covariance Matrix Factorization.- Monte Carlo Methods.- Sequential Simulation of Random Fields.- Simulated Annealing.- Karhunen-Loиve Expansion.- Karhunen-Loиve Expansion of Spartan Random Fields.- Epilogue.- A Jacobi's Transformation Theorems.- B Tables of SSRF Properties.- C Linear Algebra Facts.- D Kolmogorov-Smirnov Test.- Glossary.- References.-


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