Important resources

Learning Python

Python has more less become mandatory in the current data science landscape. As a result, many scientists rely on python scripts and notebooks to perform their work. Via collaboration and through colleagues, everyone will at one point or another be exposed to Python, hence the need to know, at the least, the basic.

Fortunately many resources are available. I would like to highlight:

• A Python manual created by Prof. Kotsuki at Chiba University (English and Japanese) available here: https://kotsuki-lab.com/education/internal-pages/

• The official documentation itself: https://www.python.org/doc/ and in particular the many tutorials that are featured.

Learning FORTRAN

Many models and tools were developed with FORTRAN. For example, models such as the Soil and Water Assessment Tool (SWAT), H08, CaMaFlood, and more recently AquaCrop were all  developed with FORTRAN. A basic understanding of the language is therefore very useful for reading their source codes and eventually for improving them. The short tutorial is straight forward and very effective.

The Generic Mapping Tools (GMT)

Learning the Generic Mapping Tools (GMT) is probably not as critical as it used to be since Python, R, and other programming languages now feature comprehensive libraries for displaying global maps. However, I notice that some of these libraries are not completely mature yet and can be rather slow for displaying high resolution dataset. As a result, learning GMT may still valuable and I would like to showcase some resources I found online: 

• A short tutorial, courtesy of Yushiro Fujii from the International Institute of Seismology and Earthquake Engineering (IISEE).

• A series of YouTube videos narrated by Dr. Paul Wessel (among many others), a key author of GMT, available on the UNAVCO channel. In addition the material of the course can be found on GMT GitHub repository here.

Suggested textbooks

R for Data Science (2e)

• Book available online, courtesy of the Author: https://r4ds.hadley.nz/ 

This is an excellent textbook that both provides i) a general and gently introduction to R and ii) a robust explanation of data science. The topics introduced feature lot's of concrete examples with plenty of code to learn from. Furthermore, the topics covered are well selected as they built on one another. As a result, the reader proficiency with R clearly expend after each reading sessions. 

An Introduction to Statistical Learning (available for R and python) 

• Book available online, courtesy of the Authors: https://www.statlearning.com/ 

This textbook provides an excellent introduction or refresher on statistics. Importantly, proofs are rarely provided and the textbook rather focus on concretely applying concept to real data. Some sections are a bit lengthy but the fist chapters are definitely a must read.

Python for Data Analysis (3e)

• No book available for free online as far as I know.

Nowadays there are just so many textbooks available for learning Python. This one is particularly well written and presented while being fairy concise.  As its title suggest, only the basic and core fundamental of python are actually introduce as the book main focus is on data analysis using popular packages.