Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning
Author:   Maurizio Petrelli
Publisher:   Springer Nature Switzerland AG
Edition:   2021 ed.
ISBN:  

9783030780548


Pages:   229
Publication Date:   17 September 2021
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning


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Author:   Maurizio Petrelli
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   2021 ed.
Weight:   0.541kg
ISBN:  

9783030780548


ISBN 10:   3030780546
Pages:   229
Publication Date:   17 September 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Part I Python for Geologists, a kick-off.- Setting Up Your Python Environment, Easily.- Python Essentials for a Geologist.- Start Solving Geological Problems Using Python.- Part II Describing Geological Data.- Graphical Visualization of a Geological Dataset.- Descriptive Statistics.- Part III Integrals and Differential Equations in Geology.- Numerical Integration.- Ordinary Differential Equations (ODE).- Partial Differential Equations (PDE).- Part IV Probability Density Functions and Error Analysis.- Probability Density Functions and their Use in Geology.- Error Analysis.- Part V Robust Statistics and Machine Learning.- Introduction to Robust Statistics.- 12. Machine Learning.

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Author Information

Maurizio Petrelli works as a researcher in petrology and volcanology at the Department of Physics and Geology, University of Perugia. In 2001, he graduated in Geology and obtained his PhD in February 2006 at the University of Perugia.His current studies are focused on the petrological, volcanological and geochemical characterization of magmatic systems with particular emphasis on time-scales estimates of magmatic processes. He combines the use of numerical simulations, experimental petrology and the study of natural samples. Since 2016, he has developed a new line of research at the Department of Physics and Geology, University of Perugia focused on the application of Machine Learning techniques to petrological and volcanological studies.

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