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.
Data-driven Astronomy
悉尼大学课程信息
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中级
完成时间大约为22 小时
英语（English）
字幕：英语（English）
您将获得的技能
Python ProgrammingMachine LearningApplied Machine LearningSQL
学生职业成果
32%
完成这些课程后已开始新的职业生涯
23%
通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为22 小时
英语（English）
字幕：英语（English）
提供方
悉尼大学
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 4 小时
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.
完成时间为 4 小时
8 个视频 （总计 30 分钟）, 1 个阅读材料, 4 个测验
8 个视频
Course overview2分钟
Pulsars3分钟
Diving in: imaging stacking5分钟
Challenge: the median doesn't scale2分钟
The solution: improving your method3分钟
Module summary1分钟
Interview with Aris Karastergiou6分钟
1 个阅读材料
Further reading10分钟
1 个练习
Pulsars: test your understanding10分钟
完成时间为 4 小时
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.
完成时间为 4 小时
7 个视频 （总计 35 分钟）
7 个视频
Supermassive black holes3分钟
What is cross-matching?4分钟
Evaluating time complexity5分钟
A (much) faster algorithm6分钟
Module summary2分钟
Interview with Brendon Brewer8分钟
1 个练习
Supermassive black holes: test your understanding10分钟
完成时间为 4 小时
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.
完成时间为 4 小时
7 个视频 （总计 35 分钟）
7 个视频
Exoplanets4分钟
Querying database with SQL4分钟
More advanced SQL4分钟
Joining tables in SQL6分钟
Module summary2分钟
Interview with Jon Jenkins8分钟
1 个练习
Exoplanets - test your understanding10分钟
完成时间为 4 小时
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.
完成时间为 4 小时
6 个视频 （总计 29 分钟）
6 个视频
The lifecycle of stars6分钟
Setting up your own database5分钟
Exploring a star cluster4分钟
Module summary2分钟
Interview with Emily Petroff6分钟
1 个练习
Stars - test your understanding10分钟
审阅
来自DATA-DRIVEN ASTRONOMY的热门评论
由 MC 提供Feb 28, 2020
Its been amazing to learn about the celestial objects, stars, galaxies. The lectures and quizzes spurred in me to explore new material online. Great hands on exercises in python and machine learning
由 TS 提供Dec 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!
由 GB 提供Apr 21, 2017
Great and quick way to learn things. Thanks for the troubles taken to put this together. Some of the computational exercises could do with a little more clarity of language. But, overall, Great!
由 GM 提供Jun 29, 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.
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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.
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