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.

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.

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.

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.

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.

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!

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.

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

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.

This course pretty much restored my faith in the online courses. Hands down, this is the best online course I have ever had, not only in astronomy. This course had everything to satisfy my needs:

*I had an opportunity to analyse actual astronomical data, which made me feel like a small child again

*I have learned to program in python for scientific reasons, which I previously only used for the backend purposes. The python skills also helped my in my computer modelling studies at the university.

*Also, even though I am working with SQL pretty much every day, this course still managed to teach me some new concepts, and understand joins better. This course, even that it's on the astronomy, managed to explain SQL much better than the conventional lectures.

Regarding the lecturers, both lecturers seemed extremely passionate in what they are talking about, and also managed to explain everything in very concise manner.

Also, I really enjoyed the quizzes, since they do not directly ask what was mentioned in the video, but the questions are twisted a bit to make you actually think for answer.

Finally, this course presents a lot of further research to investigate yourself. This ranges from scientific papers to the links to classify galaxies.

Overall, in my opinion, this course was truly amazing and made my weekends (that's when I had some time to do the exercises) worthwhile. Would definitely recommend it to anyone starting their journey in astronomy, astronomical data, python programming. If you are quite familiar with machine learning concepts and python though, this course might be a bit too easy for you. Though actual astronomical data might spice things up a little.

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.

Gostei demais das aulas condizentes com a posterior apresentação do conteúdo e aplicação dos exercícios. Acredito que alguns deles contém alguma falha na avaliação como uso de funções que não foram apresentadas, isso implica que um estudante atento possa pensar que não pode usar aquela função (como uma trapaça) ou mesmo um estudante ainda leigo na específica tarefa. Acho isso um probleminha pontual e que não interferiu de maneira alguma no aprendizado, afinal, a solução está lá para ser verificada.

O conteúdo é bem abrangente e aborda de maneira geral uma gama interessante de assuntos da astrofísica. Achei que no começo iria trabalhar somente com pulsares hehe. O que mais me instigou foi a aplicação das técnicas passadas a astrofísica, o seu embasamento e suas limitações. Foi um apanhado geral e pode ser considerado como introdutório.

Finalmente sei por onde continuar porque ter começado aqui foi um acerto e tanto!

An excellent introduction to the use of software in handling the vast quantities of data generated in astronomy. It isn't possible for a course intended to take a few hours per week to cover things in great depth.

Some interesting astronomy is covered. There are astronomy quizzes but not much physics / astronomy knowledge is necessary.

This is mainly a programming course. The 1hr estimate for Python exercises is optimistic especially for students who have little programming experience. Ideally, students should have done an introductory Python course. The importance of software performance when handling large amounts of data is emphasised.

The course also covers SQL - a useful skill, but no background necessary IMO.

The final two weeks cover machine learning applied to astronomy: how it can be useful, some pitfalls, and checking the machine learning model's quality.

I highly recommend this course if you are curious about some of the big data tools and techniques used in astronomy. Especially if you already use Python a bit and want to try out some machine learning and other astronomy related python tools. I wanted to learn something about astronomy and to play with the data - the cross-matching and machine learning were my favourite parts of the course. As usual, I'm in awe about what we know about the universe - so to casually play with data on Active Galactic Nuclei for example, or redshifts of galaxies was great fun, educational and just brilliant. I've got things I want to try out now, before starting another course. Oh, and the two tutors present the material very well on the videos.

This is a very interesting introduction to data analysis and machine learning for astronomy. The hands-on approach makes the course quite engaging.

The course is well structured and presented. The lectures are interesting and the explanations clear. The course materials are well chosen to illustrate what is being taught in the lectures. The development environment (Grok) is usable and glitch-free.

The choice of programming language, Python3, looks quite right to me. Python has become the "new normal" in astronomy. It offers an easy learning curve and a myriad of well tested modules which are available for free.

I really enjoyed this course and I would recommend it to any one with an interest in this or related subjects.