Notepad
The notepad is empty.
The basket is empty.
Free shipping possible
Free shipping possible
Please wait - the print view of the page is being prepared.
The print dialogue opens as soon as the page has been completely loaded.
If the print preview is incomplete, please close it and select "Print again".

Introduction to Python in Earth Science Data Analysis

From Descriptive Statistics to Machine Learning
BookPaperback
EUR64,00

Product description

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.
Read more

Details

ISBN/GTIN978-3-030-78057-9
Product TypeBook
BindingPaperback
PublisherSpringer
Publication townCham
Publication countrySwitzerland
Publishing date17/09/2022
Edition1st ed. 2021
Pages229 pages
LanguageEnglish
Illustrations104 farbige Abbildungen, 8 s/w Abbildungen
Article no.23415937
CatalogsVLB
Data source no.097188baf93e46c1ace554a30b4fb7cf
Product groupBU669
More details

Series

Ratings

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.

Subjects