Hi, I'm Dr Kerrie Geil, a Research Associate Professor at Mississippi State University's Geosystems Research Institute (GRI). This repo contains python learning materials that I have developed for scientists, engineers, or others who already have knowledge of both statistics and programming, but are looking to quickly pick up python skills at a level useful for scientific analysis.
I am a climate scientist, so these materials focus heavily on data and techniques that are popular in climate and earth-related sciences. The learning materials are written in the context of scientific analyses, assuming the learner already knows some statistics, the basics of computer programming, and can pick up syntax without explicit instruction. This is very different than traditional computer science learning. Anyone is welcome to use these materials but they will be most beneficial for learners who want to conduct scientific analyses using multi-dimensional labeled arrays (e.g. netcdf and tiff data).
The currently available learning modules are in the learn_by_doing directory. There is one main notebook in the root of each learning module (i.e. learnbydoing/enso/enso_analysis.ipynb). Answer keys to exercises are located in the assignments directories (e.g. learnbydoing/climate_change_indicators/assignments/).
If you are already a conda, jupyter, python and git/github user, great! Go ahead and fork this repo, build the conda environment using conda_env/learnbydoing.yml, and proceed to the learning modules in the learn_by_doing directory.
If you are new to conda, jupyter, python, or git/github, this repo contains instructions for setting up your computer to run the learn_by_doing jupyter notebooks. First follow the instructions at computer_set_up/computer_setup_instructions.MD. Then, you can copy this whole repo to your computer by clicking on the green code button (at the root level of this repo) and selecting the download zip option and then unzipping the contents. You can then proceed to running the jupyter notebooks in the learn_by_doing directory, as mentioned above.
Each year I plan to add one or two new learning modules to this repo, so check back for new learning materials.