Data-driven Astronomy

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.

Created by:  The University of Sydney

• 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 4.9 stars Average User Rating 4.9See what learners said Задания курса

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Syllabus


WEEK 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 reading expand

1. Video: Thinking about data
2. Вопрос для обсуждения: Introduce yourself (optional)
3. Video: Course overview
4. Video: Pulsars
5. Video: Diving in: imaging stacking
6. Video: Challenge: the median doesn't scale
7. Вопрос для обсуждения: How could you improve the algorithm?
8. Video: The solution: improving your method
9. Элемент LTI: Calculating the median stack
10. Video: Module summary
11. Материал для самостоятельного изучения: Further reading
12. Video: Interview with Aris Karastergiou

Graded: Set up your online assessment
Graded: Pulsars: test your understanding
Graded: Calculating the mean stack

WEEK 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 expand

1. Video: Big data makes things slow
2. Вопрос для обсуждения: What scaling problems have you encountered?
3. Video: Supermassive black holes
4. Video: What is cross-matching?
5. Video: Evaluating time complexity
6. Video: A (much) faster algorithm
7. Элемент LTI: Crossmatching with k-d trees
8. Video: Module summary
9. Video: Interview with Brendon Brewer

Graded: Supermassive black holes: test your understanding
Graded: A naive cross-matcher

WEEK 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 expand

1. Video: Organising your data
2. Вопрос для обсуждения: Do you use databases in your work?
3. Video: Exoplanets
4. Video: Querying database with SQL
5. Video: advanced SQL
6. Video: Joining tables in SQL
7. Элемент LTI: Joining tables with SQL
8. Video: Module summary
9. Video: Interview with Jon Jenkins

Graded: Exoplanets - test your understanding
Graded: Writing your own SQL queries

WEEK 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 expand

1. Video: Managing your big datasets
2. Video: The lifecycle of stars
3. Video: Setting up your own database
4. Video: Exploring a star cluster
5. Элемент LTI: Combining SQL and Python
6. Video: Module summary
7. Video: Interview with Emily Petroff

Graded: Stars - test your understanding
Graded: Setting up your own database

WEEK 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 expand

1. Video: Learning from data
2. Video: The cosmological distance scale
3. Video: What is machine learning?
4. Video: Decision tree classifiers
5. Video: Estimating redshifts using regression
6. Элемент LTI: Improving and evaluating our classifier
7. Video: Summary
8. Video: Interview with Ashish Mahabal

Graded: Cosmological distances - test your understanding
Graded: Building a regression classifier

WEEK 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 reading expand

1. Video: Classifying your data
2. Video: Types of galaxies
3. Video: Morphological classification of galaxies
4. Video: Limitations of decision tree classifiers
5. Материал для самостоятельного изучения: Classify some galaxies by hand!
6. Вопрос для обсуждения: Reflection on galaxy classification
7. Video: Improving our results with ensemble classifiers
8. Video: Module summary
9. Video: Interview with Karen Masters

Graded: Galaxies - test your understanding
Graded: Exploring machine learning classification