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Introduction to Python in earth scie...

Petrelli, Maurizio.

Introduction to Python in earth science data analysisfrom descriptive statistics to machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to Python in earth science data analysisby Maurizio Petrelli.
Reminder of title:	from descriptive statistics to machine learning /
Author:	Petrelli, Maurizio.
Published:	Cham :Springer International Publishing :2021.
Description:	xv, 229 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	GeologyData processing.
Online resource:	https://doi.org/10.1007/978-3-030-78055-5
ISBN:	9783030780555$q(electronic bk.)

Introduction to Python in earth science data analysisfrom descriptive statistics to machine learning /
Petrelli, Maurizio.

Introduction to Python in earth science data analysisfrom descriptive statistics to machine learning /[electronic resource] :by Maurizio Petrelli. - Cham :Springer International Publishing :2021. - xv, 229 p. :ill., digital ;24 cm. - Springer textbooks in earth sciences, geography and environment,2510-1315. - Springer textbooks in earth sciences, geography and environment..

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.

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.

ISBN: 9783030780555$q(electronic bk.)

Standard No.: 10.1007/978-3-030-78055-5doiSubjects--Topical Terms:

263430
Geology
--Data processing.

LC Class. No.: QE48.8 / .P48 2021

Dewey Class. No.: 550.285

Introduction to Python in earth science data analysisfrom descriptive statistics to machine learning /
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300 $a xv, 229 p. : $b ill., digital ; $c 24 cm.
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505 0 $a 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.
520 $a 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.
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