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.
提供方
Data-driven Astronomy
Learner Career Outcomes
32%
23%
您将获得的技能
教学大纲 - 您将从这门课程中学到什么
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.
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.
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.
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.
来自Data-driven Astronomy的热门评论
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.
Such a wonderful course. It had a very good mix of astronomy and computer science. The programming activities were especially good and the lectures were very informative. I highly recommend.
讲师
Tara Murphy
Associate Professor
Simon Murphy
Postdoctoral Researcher关于 悉尼大学
常见问题
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我购买证书后会得到什么？
您购买证书后，将有权访问所有课程材料，包括评分作业。完成课程后，您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
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.
还有其他问题吗？请访问 学生帮助中心。