About this Course
4.9
235 ratings
74 reviews
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....
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Intermediate Level

Intermediate Level

Clock

Suggested: 6 weeks of study, 4-6 hours/week

Approx. 19 hours to complete
Comment Dots

English

Subtitles: English

Skills you will gain

Python ProgrammingMachine LearningSqlApplied Machine Learning
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Clock

Suggested: 6 weeks of study, 4-6 hours/week

Approx. 19 hours to complete
Comment Dots

English

Subtitles: English

Syllabus - What you will learn from this course

1

Section
Clock
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. ...
Reading
8 videos (Total 30 min), 1 reading, 4 quizzes
Video8 videos
Course overview2m
Pulsars3m
Diving in: imaging stacking5m
Challenge: the median doesn't scale2m
The solution: improving your method3m
Module summary1m
Interview with Aris Karastergiou6m
Reading1 reading
Further reading10m
Quiz1 practice exercise
Pulsars: test your understanding10m

2

Section
Clock
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. ...
Reading
7 videos (Total 35 min), 3 quizzes
Video7 videos
Supermassive black holes3m
What is cross-matching?4m
Evaluating time complexity5m
A (much) faster algorithm6m
Module summary2m
Interview with Brendon Brewer8m
Quiz1 practice exercise
Supermassive black holes: test your understanding10m

3

Section
Clock
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....
Reading
7 videos (Total 35 min), 3 quizzes
Video7 videos
Exoplanets4m
Querying database with SQL4m
More advanced SQL4m
Joining tables in SQL6m
Module summary2m
Interview with Jon Jenkins8m
Quiz1 practice exercise
Exoplanets - test your understanding10m

4

Section
Clock
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. ...
Reading
6 videos (Total 29 min), 3 quizzes
Video6 videos
The lifecycle of stars6m
Setting up your own database5m
Exploring a star cluster4m
Module summary2m
Interview with Emily Petroff6m
Quiz1 practice exercise
Stars - test your understanding10m
4.9
Direction Signs

17%

started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course

Top Reviews

By GMJun 30th 2017

Great course with a good balance of code and the rewards to be had from understanding how the code works - proved to be an excellent introduction to Astronomy and confidence builder in Python.

By JMJul 15th 2017

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!

Instructors

Tara Murphy

Associate Professor
School of Physics

Simon Murphy

Postdoctoral Researcher
School of Physics

About The University of Sydney

The University of Sydney is one of the world’s leading comprehensive research and teaching universities, consistently ranked in the top 1 percent of universities in the world. In 2015, we were ranked 45 in the QS World University Rankings, and 100 percent of our research was rated at above, or well above, world standard in the Excellence in Research for Australia report....

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

  • 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.