Very helpful

Excellent!

very good

Excellent

great

This course lies in the confluence of both my professional experience (software development in the IT industry) and the science that interests me the most: astronomy and astrophysics. Just a glance at the syllabus was enough to convince me that the course would be worth taking, due to its good structure and wide scope, covering current trends in both data science and computational astronomy.

From previous online course experience in these areas, I knew at the beginning that contents can be hard to grasp if the theory and practice are not well balanced, but it turned out to be a great run, with enough depth to pique one's interest while at the same time feeling comfortable using both past and newly acquired knowledge.

The course sports an excellent tool to solve and test the programming assignments that constitute most of the grades you will earn. Thanks to it, you will be freed, as a student, from the most common hassles in online courses involving coding (mainly environment setup). Which means more chances to focus on the main subjects covered and a pleasant wading through the challenges posed.

Beware that if you are already comfortable ín the programming language used (Python), you may easily be craving for more advanced assignments, but this I'm sure is easy to request from the helpful professors and staff. If you are instead a novice with regards to coding, additional parallel effort may be required, but the course contents will guide you well in the endeavor.

One aspect of the course that may be specially challenging is the relatively speedy run through the theory and concepts of Machine Learning. A myriad other online courses on the subject exist already; the course focusses instead more on the application of the techniques and nicely shows real world (or more appropriately, universe :-) ) applications which will help cementing the theories behind. I would expect that if you have not had previous contact with the subject, the contents can feel a bit daunting. But with some extra commitment (check the numerous online resources, take a parallel course...) I am pretty sure this can be overcome.

Generally, I liked the course and really enjoyed it; I think it is really well made. However, I decided to give it 4/5 stars, because it is a bit short, covers only a small part of astronomy and machine learning and because exercises are not that challenging and too much help is sometimes given. I'd give it 5 stars if the course was a bit longer (2-3 times as long), covered more astronomy and machine learning topics (with exercises as well), and maybe most importantly forced the participant to go through more materials to be able to finish the course. Nonetheless, I'd like to thank the course's creators!

the course is informative. It combines computing theories along with astronomy. It also helps deal with us about how important is data analysis from the perspective of the astronomer. The number of lecture videos are very less. And don't deep dive into the concepts of astronomy itself. We have to read a lot. Also, this course uses grok learning software, even though it's a great method for coding exercise it has many drawbacks of not fully conveying the concepts. Moreover, the course is a bit outdated when compared to current machine learning algorithms.

I actually wanted to give four and a half stars but the option is not available. Anyway, this is a great course, teaching us some the best skills about handling the innumerous space data. The only thing is, I found some difficulties to understand the coding in some tasks just because I'd a very basic idea of using Python, and the demand of skills required for a new learner of Python are increasing exponentially. Except that, it was a great experience to learn with this course! Thank you University of Sydney.

Overall the course is great, I highly recommend it to astronomers who want to learn some new tricks in data science. Meanwhile, it is not so useful for everyone else - lections are super brief and superficial, coding assignments are very easy and not challenging at all, not only you have a pretty self-explanatory template (do A, do B), but it is possible to view a complete solution right away, which is rather strange approach.

Great introduction to the world of computational astronomy. I especially enjoyed the theory and details in astronomy-related subjects at each section. The computational techniques seemed rather simple/introductory and not explained in great detail. Having a good background in computer science and machine learning techniques is a plus to smoothly go through this course.

So far, the first week has been interesting. Challenging because I am new to Python (I am a C/C++, Swift and ABAP developer) . The assignments are well guided - challenging but far from impossible.

I would like the videos to be a little longer or give a little more theoretical background but mostly it has the right amount of info.

I thought this class was a great application of Python Programming to an interesting topic that I otherwise would not have learned. However, coming from a computer science background, the programming was pretty easy. If you are looking to strictly learn more about programming, I would suggest a more difficult course.

I've really enjoyed this course! There are a lot of new things to learn in it. Just to say something not so good from my point of view is that in the exercises sometimes the libraries or code to use could be a bit more explained or documented. But in general it's very good. I recommend it.

It is a very interesting course and the way astronomical problems and coding problems are connected is seamless and very interesting. Also, the videos have great quality and the interviews were great. I only wish it was a bit longer and also pandas were part of it

Good course, with fun examples to work with. I'm not an astronomer, and mainly took the course for fun,but the things I've learned here will be useful for my own research. About the only thing I would have liked to see was more background material on each topic.

this was an amazing course which gave me a general idea about the sheer amount of data that is required in DDA. How to handle the large volumes of data and many more. And most importantly how data is being used to find answers to the secrets of the universe.

Very interesting approach to astronomy, although I would have preferred larger part of the course devoted to strictly astronomical topics and less time spent on basic quantitative notions. By the way, thank you, you gave me some hours of enjoyment.

An amazing course to get started for someone like me, passionate about astronomy and closely related to the field of computer science. Very well organized and nicely explained course with great visuals and mid-tests. Kudos to the instructors!

Could have been a bit more elaborate. At many points during the activity I felt like I was just doing what was asked without really was happening fundamentally .So overall activities could have been a bit more Indulging hard and informative.

A really good course covering a variety of subjects in both astronomy and data analysis which is exactly the combination I was looking for. A final exercise covering all the topics taught would have been a fitting end to the course.

Well-taught, good supporting resources, and heavily rooted in application. The external grading tool exceeded expectations as well. Next time, would prefer more of the code being explained by the instructors.

A good introduction in Data Astronomy with Python. I missed some lessons about python to finish some of the volunteer exercises and some contact with astronomy data from the web, but it is a great course.

Great and quick way to learn things. Thanks for the troubles taken to put this together. Some of the computational exercises could do with a little more clarity of language. But, overall, Great!

Interesting introduction to machine-learning techniques applied to astronomical data. I think very adapted to astronomers willing to learn about this topic or to astronomy students.