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 |
- Video: Thinking about data
- Discussion Prompt: Introduce yourself (optional)
- Video: Course overview
- Video: Pulsars
- Video: Diving in: imaging stacking
- Video: Challenge: the median doesn't scale
- Discussion Prompt: How could you improve the algorithm?
- Video: The solution: improving your method
- LTI Item: Calculating the median stack
- Video: Module summary
- Reading: Further reading
- Video: Interview with Aris Karastergiou
- Video: Big data makes things slow
- Discussion Prompt: What scaling problems have you encountered?
- Video: Supermassive black holes
- Video: What is cross-matching?
- Video: Evaluating time complexity
- Video: A (much) faster algorithm
- LTI Item: Crossmatching with k-d trees
- Video: Module summary
- Video: Interview with Brendon Brewer
- Video: Organising your data
- Discussion Prompt: Do you use databases in your work?
- Video: Exoplanets
- Video: Querying database with SQL
- Video: More advanced SQL
- Video: Joining tables in SQL
- LTI Item: Joining tables with SQL
- Video: Module summary
- Video: Interview with Jon Jenkins
- Video: Managing your big datasets
- Video: The lifecycle of stars
- Video: Setting up your own database
- Video: Exploring a star cluster
- LTI Item: Combining SQL and Python
- Video: Module summary
- Video: Interview with Emily Petroff
- Video: Learning from data
- Video: The cosmological distance scale
- Video: What is machine learning?
- Video: Decision tree classifiers
- Video: Estimating redshifts using regression
- LTI Item: Improving and evaluating our classifier
- Video: Summary
- Video: Interview with Ashish Mahabal
- Video: Classifying your data
- Video: Types of galaxies
- Video: Morphological classification of galaxies
- Video: Limitations of decision tree classifiers
- Reading: Classify some galaxies by hand!
- Discussion Prompt: Reflection on galaxy classification
- Video: Improving our results with ensemble classifiers
- Video: Module summary
- Video: Interview with Karen Masters
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Very good intraductional course to modern astronomy that is nowadys more and more science about data. Not so much astronomy theory but good programing tasks, sometimes challenging, especially in the starting weeks. As addition some introduction in machine learning is provided. I would recommend it to everyone interested in data analysis in modern science.
One of the best courses I've done on Coursera. Just enough astronomy to understand the problems, and then go into the exercises in a step by step way, building up complexity. Couldn't stop!
A great course that helps one understand the basics of data analysis and how they are used in observational astronomy.
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FT
A good introduction in Data Astronomy with Python. I missed some lessons about python to finish some of the volunteer exercises and some contact with astronomy data from the web, but it is a great course.