Who is this class for: This course is aimed at science students with an interest in computational approaches to problem solving, people with an interest in astronomy who would like to learn current research methods, or people who would like to improve their programming by applying it to astronomy examples.

Created by:  The University of Sydney

  • Tara Murphy

    Taught by:  Tara Murphy, Associate Professor

    School of Physics

  • Simon Murphy

    Taught by:  Simon Murphy, Postdoctoral Researcher

    School of Physics
Commitment6 weeks of study, 4-6 hours/week
Hardware ReqYou'll need to have a computer with internet access.
How To PassPass all graded assignments to complete the course.
User Ratings
4.8 stars
Average User Rating 4.8See what learners said

How It Works
Trabajo del curso
Trabajo del curso

Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.

Ayuda de tus compañeros
Ayuda de tus compañeros

Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.


Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.

The University of Sydney
The University of Sydney is one of the world’s leading comprehensive research and teaching universities, consistently ranked in the top 1 percent of universities in the world. In 2015, we were ranked 45 in the QS World University Rankings, and 100 percent of our research was rated at above, or well above, world standard in the Excellence in Research for Australia report.
Ratings and Reviews
Rated 4.8 out of 5 of 111 ratings

I really enjoyed this course. It is very well structured, with a good progression in the complexity which make it accessible even for people who have quite no skills in Python or SQL, and who are no astronomers (like me). The teachers use a wide range of astronomical subjects to illustrate the different techniques used in data analysis. They propose examples and exercises based on real datasets, which is fabulous for people like me who don't have access to such datasets (or can have access to, but no comprehension of what they show).

Teachers are also reactive in the forums, which is much appreciated. And, for a non-english speaking person, the subtitles are very usefull. The Grok interface is incredibly easy to use, with, again, a progressive complexity in the exercises, and great explanations at each step.

If I tried to find something to improve, I would say: make more obvious how the learned techniques can actually help and improve astrophysical research, maybe with more examples of publications or concrete results obtained in the research field. But it's just quibble over details :-) The interviews in the bonus are very interesting.

So, congratulations for this great work, and thank you for opening a little bit the door of your laboratory :-). Now, more than ever, I hope to work in this domain one day.

Although, I gave a five star, but I have following notes:

It was brilliantly structured on shaping and combining scientific problems with data science to tackle those issue. However, it could use few more examples to add to our current skills. Thank you again. That was the course I was looking for, after taking a course on Machine Learning by Andrea NG, from Stanford University.

I have a profound admiration for this course and its professors.

Great course and very good material