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	4.8 stars Average User Rating 4.8See what learners said

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...

8 videos, 1 reading

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 demonstra...

7 videos

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 s...

7 videos

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

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

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

Graded: Galaxies - test your understanding

Graded: Exploring machine learning classification

FAQs

How It Works

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Creators

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.

Ratings and Reviews

Rated 4.8 out of 5 of 15 ratings

I've been looking for a class like this for a while. Nice intro to python and accessing and manipulating astro data

A great course you may study if you are interested in Astronomy and Python!

This course lies in the confluence of both my professional experience (software development in the IT industry) and the science that interests me the most: astronomy and astrophysics. Just a glance at the syllabus was enough to convince me that the course would be worth taking, due to its good structure and wide scope, covering current trends in both data science and computational astronomy.

From previous online course experience in these areas, I knew at the beginning that contents can be hard to grasp if the theory and practice are not well balanced, but it turned out to be a great run, with enough depth to pique one's interest while at the same time feeling comfortable using both past and newly acquired knowledge.

The course sports an excellent tool to solve and test the programming assignments that constitute most of the grades you will earn. Thanks to it, you will be freed, as a student, from the most common hassles in online courses involving coding (mainly environment setup). Which means more chances to focus on the main subjects covered and a pleasant wading through the challenges posed.

Beware that if you are already comfortable ín the programming language used (Python), you may easily be craving for more advanced assignments, but this I'm sure is easy to request from the helpful professors and staff. If you are instead a novice with regards to coding, additional parallel effort may be required, but the course contents will guide you well in the endeavor.

One aspect of the course that may be specially challenging is the relatively speedy run through the theory and concepts of Machine Learning. A myriad other online courses on the subject exist already; the course focusses instead more on the application of the techniques and nicely shows real world (or more appropriately, universe :-) ) applications which will help cementing the theories behind. I would expect that if you have not had previous contact with the subject, the contents can feel a bit daunting. But with some extra commitment (check the numerous online resources, take a parallel course...) I am pretty sure this can be overcome.

Great class! I didn't know that these techniques were used in astronomy. I am looking forward to more science and scientific computing courses from Coursera.

Share

Data-driven Astronomy

Data-driven Astronomy

Data-driven Astronomy