This course is aimed at researchers, students, and industry professionals who want to learn about the capabilities of Python and get experience using it applied to real-world problems. This course will introduce you to foundations of Python programming. We will utilise common geoscience data types (geospatial, temporal, vector, raster, etc) to demonstrate a variety of practical workflows and showcase fundamental capabilities of Python. We will carry out exploratory, analytical, computational and machine learning analyses on these datasets. At the end of the course you will be able to adapt these workflows to your own datasets.

The course is presented for Pawsey Data Science Week 2022 by the Sydney Informatics Hub.


The Sydney Informatics Hub (SIH) is a Core Research Facility of the University of Sydney. Core Research Facilities centralise essential research equipment and services that would otherwise be too expensive or impractical for individual Faculties to purchase and maintain. We provide a wide range of research services to aid investigators, such as:

We also aim to cultivate a data community, organising monthly Hacky Hours, outside training events (eg NVIDIA, Pawsey Center), and data/coding-related events. Look out for everything happening on our calendar or contact us (at ) to get some digital collaboration going.

Data Science Week (DSW) aims to bring together a community of data scientists, technologists, educators and more to raise awareness around data science, network, and share ideas with like-minded peers. Check out and register for some of the great Data Science Week events at www.datascienceweek.org; from May 9-13, 2022.


Sign up:

https://www.eventbrite.com.au/e/introduction-to-machine-learning-with-python-for-mineral-exploration-tickets-308435367487


Trainers


Course pre-requisites and setup requirements

A fundamental grasp on Python will be beneficial for attendees, but we will step through the workflow in a simplified approach. You should be able to recognise a for-loop, and if-else statements, know about importing modules, and understand the commands for how a simple plot is made in matplotlib. The Carpentries Python course will be more than adequate: https://swcarpentry.github.io/python-novice-inflammation/ Domain knowledge of exploration or geoscience will be helpful and most relevant, but these machine-learning/data-science workflows can be applied to any similar datasets.

Training will be delivered online, so you will need access to a modern computer with a stable internet connection. You can setup and run everything locally but support during the course will only be provided for our cloud-hosted Python instances.


Venue, online via Zoom

Participants will be provided with a Zoom link. Trainers will be broadcasting from Sydney.

Zoom etiquette and how we interact

Sessions will be recorded for attendees only, and it is set up to only record the host shared screen and host audio. We will try and get these uploaded to this site as soon as possible. Please interrupt whenever you want! Ideally, have your camera on and interact as much as possible. There will be someone monitoring the chat-window with any questions you would like to post there. Three hours is a long Zoom session so we have plenty of scheduled breaks combined with a mix of content to be delivered as demos, plus sections as independent exercises, but most of the course will be pretty-hands on with everyone writing their own code. We will use Zoom break-out rooms if needed with the Trainers.


Code of Conduct

We expect all attendees of our training to follow our code of conduct, including bullying, harassment and discrimination prevention policies.

In order to foster a positive and professional learning environment we encourage the following kinds of behaviours at all our events and on our platforms:

Our full CoC, with incident reporting guidelines, is available at https://pages.github.sydney.edu.au/informatics/sih_codeofconduct/


Date & Time:

Monday May 9, 2022, 9am - 12pm (AWST-Perth time)


Setup Instructions

You will be provided with a Nimbus Cloud Virtual Machine hosting a Jupyter Notebook server. You will connect via a web-browser directly to the interface. If you would like to configure everything locally, please see the Setup Pages