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
от партнера
Data-driven Astronomy
Сиднейский университет
Об этом курсе
Недавно просмотрено: 35,113
Карьерные результаты учащихся
32%
начал новую карьеру, пройдя эти курсы
23%
получил значимые преимущества в карьере благодаря этому курсу
Приобретаемые навыки
Python ProgrammingMachine LearningApplied Machine LearningSQL
Карьерные результаты учащихся
32%
начал новую карьеру, пройдя эти курсы
23%
получил значимые преимущества в карьере благодаря этому курсу
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Промежуточный уровень
Прибл. 25 часа на выполнение
Предполагаемая нагрузка: 6 weeks of study, 4-6 hours/week
Английский
Субтитры: Английский
Программа курса: что вы изучите
4 ч. на завершение
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.
4 ч. на завершение
8 видео ((всего 30 мин.)), 1 материал для самостоятельного изучения, 4 тестов
8 видео
Course overview2мин
Pulsars3мин
Diving in: imaging stacking5мин
Challenge: the median doesn't scale2мин
The solution: improving your method3мин
Module summary1мин
Interview with Aris Karastergiou6мин
1 материал для самостоятельного изучения
Further reading10мин
1 практическое упражнение
Pulsars: test your understanding10мин
4 ч. на завершение
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.
4 ч. на завершение
7 видео ((всего 35 мин.))
7 видео
Supermassive black holes3мин
What is cross-matching?4мин
Evaluating time complexity5мин
A (much) faster algorithm6мин
Module summary2мин
Interview with Brendon Brewer8мин
1 практическое упражнение
Supermassive black holes: test your understanding10мин
4 ч. на завершение
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.
4 ч. на завершение
7 видео ((всего 35 мин.))
7 видео
Exoplanets4мин
Querying database with SQL4мин
More advanced SQL4мин
Joining tables in SQL6мин
Module summary2мин
Interview with Jon Jenkins8мин
1 практическое упражнение
Exoplanets - test your understanding10мин
4 ч. на завершение
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.
4 ч. на завершение
6 видео ((всего 29 мин.))
6 видео
The lifecycle of stars6мин
Setting up your own database5мин
Exploring a star cluster4мин
Module summary2мин
Interview with Emily Petroff6мин
1 практическое упражнение
Stars - test your understanding10мин
Рецензии
Лучшие отзывы о курсе DATA-DRIVEN ASTRONOMY
от партнера MCFeb 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
от партнера TSDec 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!
от партнера GBApr 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!
от партнера GMJun 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.
от партнера BFAug 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.
от партнера JMJul 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!
от партнера AAJun 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.
от партнера GWSep 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.
от партнера AOJan 27, 2020
Great introductory course about different real world problems faced in the field of Astornomy and how different technologies and computational solutions can be applied to them.
от партнера AAJan 21, 2019
Excellent presentation and delivery method by the instructors and very good course content. Highly recommend it anyone curious about Universe and to understand it better!
от партнера ASApr 23, 2019
Wonderful course and loved every bit of it. My only concern is that I would love to have some form of a certificate even though I can not afford to pay for the course.
от партнера JCJan 2, 2019
This class is incredible and inspirational. I learned so many highly applicable skills and I am very glad to have taken this class in its entirety. Thank you!
О Сиднейский университет
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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Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
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Оплатив сертификацию, вы получите доступ ко всем материалам курса, включая оцениваемые задания. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
Какие правила возврата средств?
Можно ли получить финансовую помощь?
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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