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Link to this course:
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The lifecycle of stars - Data-driven Astronomy

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.
Python Programming, Machine Learning, Applied Machine Learning, SQL
This is a great course for anyone wanting to do data science with astronomical datasets. The lectures are clear and interesting and the activities are well structured. I really enjoyed this course!,Great course with a good balance of code and the rewards to be had from understanding how the code works - proved to be an excellent introduction to Astronomy and confidence builder in Python.

The lifecycle of stars - Data-driven Astronomy
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