Introduction to Python in earth science data analysis: from descriptive statistics to machine learning

by Petrelli, Maurizio

Type Book

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Title Statements

Full title :Introduction to Python in earth science data analysis : from descriptive statistics to machine learning / Maurizio Petrelli.

Summary

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.

Edition Statements

Publication Date:©2021

Publication Statements

Publisher(s):Springer
Place of Publication:Cham
Language:eng
Publication Date:©2021

The Physical Object

Description:1 online resource : : illustrations (chiefly color)

Edition Identifiers

ISBN:9783030780555, 3030780554
DOI:10.1007/978-3-030-78055-5
oclc:on1268441221
LC Classification:QE48.8
Dewey Decimal:550.285

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.