Acerca de este curso: 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.
Para quién es esta clase: 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.
Creada por: The University of Sydney
Enseñado por: Tara Murphy, Associate Professor
School of Physics
Enseñado por: Simon Murphy, Postdoctoral Researcher
School of Physics
| Nivel | Intermedio |
| Compromiso | 6 weeks of study, 4-6 hours/week |
| Idioma | English |
| Requerimientos de hardware | You'll need to have a computer with internet access. |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 1
Thinking about data
This module introduces the idea of computational thinking, and how big data can make simple problems quite challenging to solve. We use the example of calculating the median and mean stack of a set of radio astronomy images to illustrate some of the issues you encounter when working with large datasets.
8 videos, 1 lectura
- Discussion Prompt: Introduce yourself (optional)
- Vídeo: Course overview
- Vídeo: Pulsars
- Vídeo: Diving in: imaging stacking
- Vídeo: Challenge: the median doesn't scale
- Discussion Prompt: How could you improve the algorithm?
- Vídeo: The solution: improving your method
- LTI Item: Calculating the median stack
- Vídeo: Module summary
- Reading: Further reading
- Vídeo: Interview with Aris Karastergiou
Calificado: Set up your online assessment
Calificado: Pulsars: test your understanding
Calificado: Calculating the mean stack
SEMANA 2
Big data makes things slow
In this module we explore the idea of scaling your code. Some algorithms scale well as your dataset increases, but others become impossibly slow. We look at some of the reason for this, and use the example of cross-matching astronomical catalogues to demonstrate what kind of improvements you can make.
7 videos
- Discussion Prompt: What scaling problems have you encountered?
- Vídeo: Supermassive black holes
- Vídeo: What is cross-matching?
- Vídeo: Evaluating time complexity
- Vídeo: A (much) faster algorithm
- LTI Item: Crossmatching with k-d trees
- Vídeo: Module summary
- Vídeo: Interview with Brendon Brewer
Calificado: Supermassive black holes: test your understanding
Calificado: A naive cross-matcher
SEMANA 3
Querying your data
Most large astronomy projects use databases to manage their data. In this module we introduce SQL - the language most commonly used to query databases. We use SQL to query the NASA Exoplanet database and investigate the habitability of planets in other solar systems.
7 videos
- Discussion Prompt: Do you use databases in your work?
- Vídeo: Exoplanets
- Vídeo: Querying database with SQL
- Vídeo: More advanced SQL
- Vídeo: Joining tables in SQL
- LTI Item: Joining tables with SQL
- Vídeo: Module summary
- Vídeo: Interview with Jon Jenkins
Calificado: Exoplanets - test your understanding
Calificado: Writing your own SQL queries
SEMANA 4
Managing your data
This module introduces the basic principles of setting up databases. We look at how to set up new tables, and then how to combine Python and SQL to get the best out of both approaches. We use these tools to explore the life of stars in a stellar cluster.
6 videos
- Vídeo: The lifecycle of stars
- Vídeo: Setting up your own database
- Vídeo: Exploring a star cluster
- LTI Item: Combining SQL and Python
- Vídeo: Module summary
- Vídeo: Interview with Emily Petroff
Calificado: Stars - test your understanding
Calificado: Setting up your own database
SEMANA 5
Learning from data: regression
This module introduces the idea of machine learning. We look at standard methodology for running machine learning experiments, and then apply this to calculating redshifts of distant galaxies using decision trees for regression.
7 videos
- Vídeo: The cosmological distance scale
- Vídeo: What is machine learning?
- Vídeo: Decision tree classifiers
- Vídeo: Estimating redshifts using regression
- LTI Item: Improving and evaluating our classifier
- Vídeo: Summary
- Vídeo: Interview with Ashish Mahabal
Calificado: Cosmological distances - test your understanding
Calificado: Building a regression classifier
SEMANA 6
Learning from data: classification
In this final module we explore the limitations of decision tree classifiers. We then look at ensemble classifiers, using the random forest algorithm to classify images of galaxies into different types.
7 videos, 1 lectura
- Vídeo: Types of galaxies
- Vídeo: Morphological classification of galaxies
- Vídeo: Limitations of decision tree classifiers
- Reading: Classify some galaxies by hand!
- Discussion Prompt: Reflection on galaxy classification
- Vídeo: Improving our results with ensemble classifiers
- Vídeo: Module summary
- Vídeo: Interview with Karen Masters
Calificado: Galaxies - test your understanding
Calificado: Exploring machine learning classification
Preguntas Frecuentes
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The University of Sydney
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Calificaciones y revisiones
A great introduction to Data-driven Astronomy and a good way for starting to learn Python by solving problems
A beautiful course with concisely taught concepts of high depth.
A very good introductory course on the applications of machine learning in astronomy.
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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.