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1,177 ratings

Tara Murphy +1 more instructor

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Data-driven Astronomy

The University of Sydney

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 24 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Skills you will gain

Python Programming
Machine Learning
Applied Machine Learning
SQL

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 24 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructors

Instructor rating

4.8/5 (351 Ratings)

Tara Murphy

Simon Murphy

Postdoctoral Researcher

School of Physics

28,067 Learners

1 Course

Offered by

The University of Sydney

Our excellence in research and teaching makes the University of Sydney one of the top universities in Australia and highly ranked among the best universities in the world. In 2020, we were ranked second in the Times Higher Education (THE) University Impact Rankings, and first in Australia in the QS Graduate Employability Rankings.

Syllabus - What you will learn from this course

Content Rating98%(3,513 ratings)

Week

Week 1

4 hours to complete

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.

4 hours to complete

8 videos (Total 30 min), 1 reading, 4 quizzes

Week

Week 2

4 hours to complete

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.

4 hours to complete

7 videos (Total 35 min)

Week

Week 3

4 hours to complete

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.

4 hours to complete

7 videos (Total 35 min)

Week

Week 4

4 hours to complete

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.

4 hours to complete

6 videos (Total 29 min)

Reviews

4.8

346 reviews

5 stars
84.76%
4 stars
13.55%
3 stars
1.17%
2 stars
0.25%
1 star
0.25%

TOP REVIEWS FROM DATA-DRIVEN ASTRONOMY

by AAJan 21, 2019

Excellent presentation and delivery method by the instructors and very good course content. Highly recommend it anyone curious about Universe and to understand it better!

by AAJun 20, 2018

This is a well set course. I have completed one week and I loved blend of maths, astronomy and tools!Course content is not outdated, which is really important for a field like this.

by SSOct 21, 2021

This course is exceptionally good, well developed and structured. The content of the course is good. The teachers have demonstrated the concept well. I would like to learn more on this concept.

by TSDec 14, 2019

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!

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
What programming background is assumed?
We assume you are familiar with basic programming in a modern programming language including variables, control structures, data structures, functions, and working with files. In this course we will use Python 3.
We'll walk through all the examples and provide lots of support, so jump in and have a go. If haven't done any programming for a while, you might want to brush up before you start.

More questions? Visit the Learner Help Center.

Data-driven Astronomy

Data-driven Astronomy

About this Course

Skills you will gain