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Data-driven Astronomy (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.

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.