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.
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Data-driven Astronomy
Сиднейский университетОб этом курсе
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Попробуйте Coursera для бизнесаПриобретаемые навыки
- Python Programming
- Machine Learning
- Applied Machine Learning
- SQL
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Программа курса: что вы изучите
Thinking about data
Big data makes things slow
Querying your data
Managing your data
Рецензии
- 5 stars85,18 %
- 4 stars13,12 %
- 3 stars1,20 %
- 2 stars0,24 %
- 1 star0,24 %
Лучшие отзывы о курсе DATA-DRIVEN ASTRONOMY
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.
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!
It's a nice and short intro to both astronomy and analysing data with python. For me it was a too short because I would like to learn more about this topic, but totally worth it.
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
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оплатив сертификацию?
Можно ли получить финансовую помощь?
What programming background is assumed?
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