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Kursteilnehmer-Bewertung und -Feedback für Data-driven Astronomy von The University of Sydney

4.8
Sterne
960 Bewertungen
284 Bewertungen

Über den Kurs

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy. Regardless of whether you’re already a scientist, studying to become one, or just interested in how modern astronomy works ‘under the bonnet’, this course will help you explore astronomy: from planets, to pulsars to black holes. Course outline: Week 1: Thinking about data - Principles of computational thinking - Discovering pulsars in radio images Week 2: Big data makes things slow - How to work out the time complexity of algorithms - Exploring the black holes at the centres of massive galaxies Week 3: Querying data using SQL - How to use databases to analyse your data - Investigating exoplanets in other solar systems Week 4: Managing your data - How to set up databases to manage your data - Exploring the lifecycle of stars in our Galaxy Week 5: Learning from data: regression - Using machine learning tools to investigate your data - Calculating the redshifts of distant galaxies Week 6: Learning from data: classification - Using machine learning tools to classify your data - Investigating different types of galaxies Each week will also have an interview with a data-driven astronomy expert. Note that some knowledge of Python is assumed, including variables, control structures, data structures, functions, and working with files....

Top-Bewertungen

SK
10. Sep. 2020

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.

MC
28. Feb. 2020

Its been amazing to learn about the celestial objects, stars, galaxies. The lectures and quizzes spurred in me to explore new material online. Great hands on exercises in python and machine learning

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226 - 250 von 281 Bewertungen für Data-driven Astronomy

von Amrit P S

27. Apr. 2020

Amazing course! great assignments.

von Ujjwal J

13. Juni 2019

best course i ve taken in coursera

von Behzad B A

27. Jan. 2019

Very professional, very productive

von Eric H

28. Okt. 2020

Very delicately designed course

von Sachin V

29. Sep. 2019

extremely multidisciplinary!

von Afiq A H

22. Apr. 2019

Good stuff. Thanks Coursera.

von Luciano S

10. Juni 2017

It was an amazing course!

von Amit S

5. Aug. 2020

a very insightful course

von Ulisses M C

17. Mai 2018

The course is excellent.

von Winston A W

19. Okt. 2020

Excellent, than you.

von hawzhin b

23. Aug. 2018

Very useful course ,

von Святослав С

6. Jan. 2018

Excellent course! =)

von Yasith R

9. Juni 2019

Excellent course..

von RAED B C

15. Okt. 2020

Great cours *****

von Diego J M G

14. Jan. 2019

Muy recomendable

von Syed Z R Z

18. Jan. 2020

Its gerearatttt

von Rohit N

11. Juni 2019

I'm loving it!

von Jiqing H

11. Juni 2019

great course!

von Israel d S R d A

7. Feb. 2020

Very helpful

von Renato T

31. Juli 2020

Excellent!

von 唐怀金

23. Apr. 2019

very good

von Kristina

19. Apr. 2017

Excellent

von Nikhil G M

13. Juni 2020

great

von Victor M

17. Mai 2017

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.

von Tomislav P

27. Jan. 2020

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!