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.
提供方
Data-driven Astronomy
悉尼大学
课程信息
49,586 次近期查看
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
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
100% 在线
立即开始，按照自己的计划学习。
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中级
完成时间大约为25 小时
建议：6 weeks of study, 4-6 hours/week
英语（English）
字幕：英语（English）
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
4.8
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.
由 BF 提供Aug 10, 2019
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.
由 JM 提供Jul 14, 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!
由 AA 提供Jun 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.
由 GW 提供Sep 29, 2019
If you want to learn Python and a bit of Machine Learning in the context of Astronomy then this is a great course to give your skills a boost and learn more about modern Astronomy.
提供方
悉尼大学
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.
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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。
