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Online Course

Data-driven Astronomy

The University of Sydney via Coursera


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.

Taught by

Tara Murphy and Simon Murphy

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5.0 rating, based on 2 reviews

Start your review of Data-driven Astronomy

  • Arnaud Dion

    Arnaud Dion is taking this course right now, spending 5 hours a week on it and found the course difficulty to be medium.

    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.
  • Kristina Šekrst completed this course and found the course difficulty to be medium.

    This is a wonderful cross-section of machine learning advancement and astrophysics. With no background in astronomy or with no background in programming, it is easy to stick around. And if you have knowledge of both, then you'll certainly enjoy your stay since there are advanced assignments as well. The course uses Python and Grok interactive learning tool, and you'll tackle a bit with SQL and machine learning. The instructors were pretty much omnipresent in the forums and helped out everybody, which is praiseworthy. I had fun while learning new things, and I hope to see a sequel to such a novel course.

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