Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning (Springer Textbooks in Earth Sciences, Geography and Environment)

Petrelli, Maurizio

ISBN 10: 3030780570 / ISBN 13: 9783030780579

Published by Springer, 2022

About this Item

Seller Inventory # I-9783030780579

Bibliographic Details

• Title: Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning (Springer Textbooks in Earth Sciences, Geography and Environment)
• Publisher: Springer
• Publication Date: 2022
• Language: English
• ISBN 10: 3030780570
• ISBN 13: 9783030780579
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

About the Author

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.