I have been an astronomy addict since I was a teenager; but, thinking about money, I had a computer science background. Now that I found out the wonderful universe of data science and specially its connection to how astronomy can progress with it, I think i'm completely back to studying our wonderful universe.

I finished this course today. If you want to learn advanced concepts like machine learning, decision tree classification, SQL, and more; then this is the course for you! I'm a senior in high school, and I'm going to major in Astrophysics. If you love Computer Science this will be an interesting course, as it will show the applications of CS to Astronomy.

A great deal of effort went into designing this course. My hat is off to the instructors who designed and participated in this course as well as the mentors who were deep in the weeds with us on our code. With a great deal of effort on your part you will learn a lot.

The single largest issue in data driven astronomy is managing and analyzing enormous datasets. From the very beginning, you will learn about scaling algorithms up to process the size of the datasets efficiently. This is a major theme in computer science.

You will learn a great deal of astronomy from exoplanets to galaxy morphology. The material is sophisticated and does not patronize you the student. It is difficult and rewarding!

My knowledge of Python is rusty as I have been focusing on R for the past year. You will learn more Python, NumPY, matplotlib, and Astropy modules.

The lectures are well thought out and deep. The instructors made interesting comments about the nature of science and the challenges of astronomy.

The bonus interviews with astronomers are not to be missed. The astronomers involved drop great tips about science, the field as it is as well as its recent history.

Answers to the quizzes are not entirely contained in the videos. As a consequence I've either had to think hard about the answers or research the material further--both activities are worthwhile.

Datasets are provided to further pursue the topics in the course, something I fully intend to do. The k-d algorithm is passed off to a AstroPy module--I think it could be done from scratch.'

Careful attention is given to the details of the statistical issues--training and test datasets, asymptotic theory of binning data to obtain medians, etc.

Its all good!

If I had any suggestions to make, it would be to encourage writing up the results of the analyses--giving the student the opportunity not just to celebrate successful code, but to understand the results as well.

Fifty-five years ago, as a school leaver about to go to University to study physics, I switched on the television in the middle of the day - when there was nothing showing in the programme schedule printed in the West Australian. To my surprise, instead of either a 'test card' or 'RF noise', there was a lecture being given by a professor, visiting from the UK, as part of the 'Sydney Summer School of Science'.

Wow! Here was exciting and informative educational television on an Australian commercial TV channel, showing at the same time every day for over a week but with no publicity. My awareness of big questions in physics took a great leap forward - by accident.

Fast forward 65 years. A recent occasional glance at Coursera's menu of courses in physics came up with 'Data Driven Astronomy' from the University of Sydney. Being locked down due to Covid-19, I ventured into the first week of the course. What a surprise! I couldn't stop following it - all day, every day for several days running.

Thank you, University of Sydney, for raising my awareness yet again about current big questions in physics (astronomy) and how they can be addressed with big data and an impressive toolkit.

First few weeks are challenging, from the coding point of view, but the knowledge that one gains about our Universe is simply fantastic. I've never enjoyed using a Programming language to solve, even though at a beginner's level, problems up until this class. Simply fantastic. If you're curious about Deep Learning, like I am, and are an aspirant in the field of Machine Learning, I highly suggest this course if you're trying to work your way around beginning your journey in Python. I'm proficient in R.

I can't believe this but I've always loved Astrophysics. After 6 years of education and getting a Master's in Industrial Engineering, this course has reignited my love to study our Universe. I will be hungry for more and will be returning to school in the near or distant future for a degree in Astrophysics. Thank You, I love Physics and I really wish I didn't waste my time pursuing what I did pursue.

This is a well set course. I have completed one week and I loved blend of maths, astronomy and tools!Course content is not outdated, which is really important for a field like this.

The course is very simple for someone that work as a programmer in data science. Nevertheless is very interesting for who haven't seen astronomical data and want to do some short analysis.

Dr Tara Murphy is exceptionally good at extracting and compressing essential informations and transporting it to the audience. A very well structured course with phantastically produced short movies about basic astronomy topics on an introductory level (great fun to watch this powerthirstesque kind of galactic round-house kick) Reveals some very important fundamentals you should know about scientific computing, introduces you to some of the really hot public scientific libraries, and, eventually, adds some GROK platform learning experience which is unparalleled. There's only one downer (two if you add Dr Simon Murphy's noctilucent shirt in his first lecture): it only scratches a few microns of that nasty double-headed science dragon. Don't expect to to be able to solve problems on the scale of the real world, er... universe with the obtained knowledge. Nevertheless, great job Data-driven Astronomy team!

This is real astronomy ! A fantastic approach to current research subject. If you want to learn astronomy from the ground up, take an introductory course before this one. It starts directly to studying pulsars statistics, and most important, how to detect and study it. All the worshops are in Python, using a web notebook. But it's neither an introductory course on Python. So, it' better to have a minimum knowledge on programming and Python language. But, if you have the prequisites, and are interested to do computation for astronomy using large datasets, this is the course. The techniques can also been extended to other computational intensive domains.

Excellent course that provides an introduction to astronomy from a data analysis point of view. The concepts of astronomy that are touched in this course are not very deep. However, they are well chosen so that the course can be done without any problem. On the other hand, the concepts of data analysis and machine learning are very well explained, so that what you learn here will serve as a basis to face new learning challenges. As I said, just excellent!

Great hands-on introduction to what it means to be an astronomer these days ("the telescope is the data")!

Had a good time solving the exercises, learning the theory, and discovering the many references provided.

Well done, great job!

It is a course that I have taken with great pleasure. My background in Astrophysics and my work as a data scientist in private enterprise give me some qualified opinion to say that it is a very well done course. It touches on the basic Python programming topics that will be used, exemplifies with real data the use of data science in solving relevant questions in Astronomy. I also liked the interviews with scientists who use these tools and their opinions about it. I would have loved to have a course like this at my university. Recommended from every point of view, as an introductory course where data analytics meets Machine Learning and the exploration of the Universe.

This is a great course where we learn about astronomy as well as how to process astronomical data using python programming language, machine learning and SQL. The quizzes and programming assignments really help to apply and grasp the concepts learnt in the lectures better. It is a great introductory course for anyone who is passionate about astronomy and programming .

I enjoyed the course. I felt this was a data course with an astronomy wrapper, which is great, because the data portion is applicable way beyond astronomy. The course provides a good intro into numpy (a super useful python library) and sklearn (a super useful machine learning library). I would take this course again.

This is a great course combining interesting topics in astronomy with corresponding python challenges. Numpy, SQL and machine learning are all covered here in an astronomy context, though it's easy to see how the same techniques could be applied to other fields.

Very interesting course that offers plenty of practical applications and insights into data handling for astronomy. It sparks the interest with interviews of experts and additional material on the astronomical topics studied.

Really amazing course! Gave me insights into how data analysis works in the field of astronomy and how one can use different machine learning techniques to classify the huge amounts of data generated.

This is a great course for anyone wanting to do data science with astronomical datasets. The lectures are clear and interesting and the activities are well structured. I really enjoyed this course!

One of the best courses I've done on Coursera. Just enough astronomy to understand the problems, and then go into the exercises in a step by step way, building up complexity. Couldn't stop!

Best MOOC I've ever done. Great for anyone interested in astronomy and/or machine learning.

Very delicately designed course

The course was interesting, but suffered from being a little to lightweight in both the Astronomy and Data Driven aspects - it probably tries to do too much in a short period of time. If you already have some programming knowledge, esp in SQL or Python, the practical assignments you'll likely find quite easy and can be completed in under half the estimated time. I'd be interested in seeing an advanced version of this course though!

Although the course content and structure is well organized and easily to follow, there are not enough lectures on the actual coding itself? The videos only really tell you the purpose and background of your code rather than how the coding process itself goes, for which, you are really just left with reading material that you eventually start to trip over and get disinterested in.

I liked the course. Although I feel like if the course was longer and more extensive, I could have learnt a lot more. There aren't many courses that guide one in astronomical data analysis and I have a lot more to learn now.

A decent introductory course. The weeks follow themes and are not indicative of a suggested timeline.