The University of Sydney

Data-driven Astronomy

Taught in English

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35,178 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Tara Murphy
Simon Murphy

Instructors: Tara Murphy

4.8

(1,336 reviews)

Intermediate level
Some related experience required
23 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Assessments

6 quizzes

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There are 6 modules in this course

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.

What's included

8 videos1 reading1 quiz3 app items2 discussion prompts

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.

What's included

7 videos1 quiz2 app items1 discussion prompt

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.

What's included

7 videos1 quiz2 app items1 discussion prompt

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.

What's included

6 videos1 quiz2 app items

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.

What's included

7 videos1 quiz2 app items

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.

What's included

7 videos1 reading1 quiz1 app item1 discussion prompt

Instructors

Instructor ratings
4.8 (435 ratings)
Tara Murphy
The University of Sydney
1 Course35,178 learners
Simon Murphy
The University of Sydney
1 Course35,178 learners

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Recommended if you're interested in Physics and Astronomy

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