Brought to you by Marco A. Lopez-Sanchez - This website was last modified: 2024-02-14
This is an introductory course to the Python programming language for data analysis using examples from the earth sciences. This course aims to give an overview (breadth rather than depth) of the Python programming language and the main scientific libraries so that you can start using it in your workflow and add a new free scientific analysis tool to your portfolio. The principles of the course are:
-
establishing a solid language foundation
-
learning by doing (focus on practice)
-
top-down teaching approach
Note
At the moment, this repository contains the unfinished walkthrough notebooks that guide the course but without detailed explanations (i.e. slides) or solutions to the exercises. The course is free and licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. Although they do not contain the full course, the walkthrough notebooks are self-explanatory, so anyone can use them to learn about the subject at their own pace. Please do not post solutions to the exercises in any public forum or publicly accessible software repository.
-
The Python programming language: getting started (mostly done)
-
Matplotlib overview: the scientific plotting library (mostly done)
-
Numpy ("numerical Python") overview (in development)
-
Pandas overview (in development)
-
Scipy ("scientific Python") examples (outlined)
The course assumes no previous knowledge of the Python or any other programming language. We assume that the reader is familiar with basic linear algebra and statistics.
This is not a Python course on image analysis, geochemistry, machine learning or any other specific topic that you can think of. Although you will find examples of different topics, the course is aimed at giving an overview of the language and the Python scientific ecosystem so that you can start using it and go deeper into the topic you want. Nor is it a course where you will learn all the details and machinery behind the Python language (bottom-down approach). Python is simply the programming tool of choice for data analysis because it is free, easy to learn, and a great tool to introduce people to the world of scientific programming.
I am Marco A. Lopez-Sanchez, a researcher with expertise in the analysis of the microstructure and texture (CPO) of solid materials. I have broad experience in programming and code development for data analysis (https://github.com/marcoalopez). I started this project for a mixture of fun, preaching open science and scientific programming, and learning. I live and work in Oviedo, Spain. All there is to know about me can be found at https://marcoalopez.github.io/
The GitHub website hosting the project provides several options (you will need a GitHub account, it’s free!):
- Open a discussion: This is a place to:
- Ask questions you are wondering about.
- Share ideas.
- Engage with the developers (still just me).
Besides, if you want to contribute to the project, you might want to glimpse at the code of conduct (TLDR: be nice to others 😉).
Copyright © 2024 Marco A. Lopez-Sanchez
Warning
Information presented on this website and the notebooks is provided without any express or implied warranty and may include technical inaccuracies or typing errors; the author reserve the right to modify or enhance the content of this website as well as the notebooks at any time without previous notice. This webpage and the notebooks are not liable for the content of external links.
Hosted on GitHub Pages — This website was created with Typora