Welcome to the Geoscientist to Data Scientist 12 week program! This document decsribes how to access all the course content for the next 12 weeks.

Getting access: All course content is delivered using GitHub. If you don't already have a GitHub account you will also need to sign up for one at https://github.com/join.

Please let Jess know what your GitHub username is so that he can add you to the core-skills GitHub organization. Then you should have read access to all the repositories for each week.

Getting help: we'll have a Gittr chat room set up for the duration of the course where you can get help from course facilitators and other course participants. You can sign up with your Github account here:

All the course content is deliverd via seperate repositories for each week (see below for instructions on getting set up each week). Here's the links to each week:

Week	Link to github	Binder
18 September	Zero to data science
25 September	Getting to know the tools
2 October	Simple predictions
9 October	Multivariate analysis
16 October	Effective data storytelling
23 October	Machine learning
30 October	Deep learning
6 November	Time-series and network data
13 November	Data fusion sandbox
27 November	Natural language processing
4 December	Spatial data

We recommend that you aim to get all the code examples running locally - we'll provide online environments for those that can't install things on their work machines but make no claims as to the security or safety of using these environments. In particular saving your work will be easier.

There are two prerequisites - you will need both Git (a software collaboration/versioning tool) and a Python installation. There will be a prerequisite two-day course (run by the Curtin Uni Data Carpentry team) to introduce you to these tools and get you up and running, but if you're keen to get started we recommend:

• GitKraken for managing Git repositories - this is a nice cross-platform GUI which removes some of the confusion of the commandline. It's free for non-commercial use and pretty cheap for commercial licenses. There are other git clients out there but this is the easiest we've found so far. They also have a good getting started guide. Get it here
• Anaconda for managing Python and R dependencies - we will be managing several Python environments over the course of the program and conda provides a way of doing this easily. Get it for your platform here

You will also need to sign up for a GitHub account at https://github.com/join. Please let Jess know what your GitHub username is so that he can add you to the core-skills GitHub organization.

You should make sure that GitKraken has access to your Github account so that it can see what repositories you have read access to. You can then use their GUI to clone the relevant repositories to your local machine.

Some notes:

• GitKraken have a good cheat sheet for GitHub users here: https://www.gitkraken.com/resources/gitkraken-github-cheat-sheet
• We recommend that you clone your repositories into your home folder -

Once you've got these tools you can proceed to getting set up each week

All you should need to do is open the relevant repository in GitKraken, hit 'pull' to merge in any new changes and you're good to go with the latest version. Contact a facilitator if you have any issues with this.

Then you need to do two things to get set up: (a) build the conda environment and (b) run the notebook server to run the examples. This will be covered in detail in the pre-requisite days and will hopefully become second nature to you over the course of the course. However if you have any issues please contact one of the facilitators.

This makes sure that you have all the Python libraries available on your machine that are required for the week's code examples.

Now you can

Each week has its own repo. Inside the folder are several different files and subfolders:

• README.md - do as it says! We'll put important information, suggested reading and other special bits and bobs for the week in here.

• environment.yml - this contains the Python dependencies required to run the jupyter notebooks and code examples

• data - contains the data for the week, feel free to have a poke around. Note that some data sources we use may be live databases online - we'll try and provide you with instructions for setting these up for yourself where we use these.

• notebooks/*.ipynb - these are the Jupyter notebooks with the code examples. If you click these on Github they will get rendered nicely to HTML. But if you want to run the code you will need to either do so in Binder or locally on your machine.

You will need to fork this repo into your own github account to be able to push changes into it. All you need to do is click the Fork button (see below) and you've got your own copy.

Then you need to clone the repository to your local machine. If you're using GitKraken, you can sign in with your Github account to make this easier, following these instructions. Make sure that you pick your personal repository so you can push changes back to it (participants have read-only access to the main core-skills repository).

If you want to push changes back to the main repository then you can take a look at submitting a pull request in Github