About this course: 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.
Who is this class for: This course is aimed at science students with an interest in computational approaches to problem solving, people with an interest in astronomy who would like to learn current research methods, or people who would like to improve their programming by applying it to astronomy examples.
Taught by: Tara Murphy, Associate Professor
School of Physics
Taught by: Simon Murphy, Postdoctoral Researcher
School of Physics
| Level | Intermediate |
| Commitment | 6 weeks of study, 4-6 hours/week |
| Language | English |
| Hardware Req | You'll need to have a computer with internet access. |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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A perfect course for those who are interested in how astronomers deal with the enormous amount of data coming from the modern instruments. Each week is a high-level touch on a astronomical subject and data techniques that may be associated with it. A good starting point for those who would like to dig further.
A bit more reference materials on publicly available raw data sources would be appreciated.
It was an amazing course!
Great course !! really enjoyed learning astronomy with data science..
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Thank you for a very interesting, educational, and ofttimes challenging course. I suspect the instructors and mentors have introduced me to only the tip of the iceberg - 98% of data-driven astronomy lies below the surface and inside enormous datasets/databases. It almost makes me wish life had taken a different course and brought me to this fascinating subject. I would look forward to any further courses from you.