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Opiniones y comentarios de aprendices correspondientes a Data-driven Astronomy por parte de Universidad de Sidney

869 calificaciones
258 reseña

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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....

Principales reseñas


Sep 18, 2020

Very Nice course, materials well explained though the programming exercises were very difficult for me, as I did not had that much in depth knowledge of python, for which I had to take additional help


Sep 11, 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.

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201 - 225 de 255 revisiones para Data-driven Astronomy

por Mark M

Mar 18, 2019

Interesting, engaging and informative!

por Rodrigo J

Aug 12, 2017

Fantastic and concise hands-on course!

por Ayomal S

Sep 03, 2020

simply amazing and great explanation

por Ernesto P

Sep 28, 2017

Great course and very good material

por Amrit P S

Apr 27, 2020

Amazing course! great assignments.

por Ujjwal J

Jun 13, 2019

best course i ve taken in coursera

por Behzad B A

Jan 27, 2019

Very professional, very productive

por Sachin V

Sep 29, 2019

extremely multidisciplinary!

por Afiq A H

Apr 22, 2019

Good stuff. Thanks Coursera.

por Luciano S

Jun 11, 2017

It was an amazing course!

por Amit S

Aug 05, 2020

a very insightful course

por Ulisses M C

May 17, 2018

The course is excellent.

por hawzhin b

Aug 24, 2018

Very useful course ,

por Sviatoslav S

Jan 06, 2018

Excellent course! =)

por Yasith R

Jun 10, 2019

Excellent course..

por Diego J M G

Jan 14, 2019

Muy recomendable

por Syed Z R Z

Jan 18, 2020

Its gerearatttt

por Rohit N

Jun 12, 2019

I'm loving it!

por Jiqing H

Jun 12, 2019

great course!

por Israel d S R d A

Feb 07, 2020

Very helpful

por Renato T

Jul 31, 2020


por 唐怀金

Apr 23, 2019

very good

por Kristina I

Apr 19, 2017


por Nikhil G M

Jun 13, 2020


por Victor M

May 17, 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.