Our skills, expertise, and offerings

Comprehensive and well taught computational training is paramount for the astronomy community to harness the potential of the next generation telescopes and astronomy simulations. In the era of big telescopes and big data, data analysis practices need to scale to the volume of data processing and analysis needed for researchers to compete in a world-class arena.

Many undergraduate and postgraduate degrees in physics and astronomy do not offer modules on computing and current computational advancement, hence researchers at any level are mostly self-taught programmers. This proves to be a major problem when rigorous computational methods are required to ensure efficient and consistent processing and analysis of data.

Training provided by ADACS aims to teach astronomers important basic computing practices, as well as offer them content on new computational advances that could prove to be vital in future data analysis.

Our main streams of delivery are face-to-face and online. Please have a look at the respective pages to learn more and connect with the content.

Over the past 2 years  ADACS has delivered more than a dozen face-to-face events, ranging from outreach events, to half-day and multi-day workshops.

We have been developing a series of webinars to help the community master a variety of topics related to computing for astronomers. These webinars are a short series of informational videos compiled by theme on the ADACS LMS.

Our youtube channel (ADACS learning) makes a selection of our LMS webinars available to the public.

Material we teach during these workshops is generally made available via our Github page

ADACS runs regular internships offering student the possibility to work on software development projects within astronomy

A curated list of tutorials,information and cheat sheets from around the web on key skills for astronomy reaserchers that are not explicitly covered in our webinars.

Astronomer Skills Profile

ADACS is currently putting together a skills tree of astronomers at various career stages, with the aim to map out the associated learner journey.
This will help identify where on this learner journey ADACS currently provides training and which future training we should focus on developing/providing.

Participation in this survey is completely voluntary and confidential. No personal data will be shared.
However, the outcome of this survey, i.e., the skills tree and learner journey, will be made available via the ADACS webpage.

On the right is an average computing skills/confidence profile for a PhD student.

To help us build up the skills profile of astronomers at different career stages, please consider filling out our survey.

Python Programming
Version Control
High Performance Computing