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.
oferecido por
Data-driven Astronomy
Universidade de SydneyInformações sobre o curso
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Resultados de carreira do aprendiz
23%
comecei uma nova carreira após concluir estes cursos
17%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 24 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
Habilidades que você terá
Python ProgrammingMachine LearningApplied Machine LearningSQL
Resultados de carreira do aprendiz
23%
comecei uma nova carreira após concluir estes cursos
17%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 24 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
oferecido por
Universidade de Sydney
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.
Programa - O que você aprenderá com este curso
5 horas para concluir
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.
5 horas para concluir
8 vídeos (Total 30 mín.), 1 leitura, 4 testes
8 videos
Course overview2min
Pulsars3min
Diving in: imaging stacking5min
Challenge: the median doesn't scale2min
The solution: improving your method3min
Module summary1min
Interview with Aris Karastergiou6min
1 leituras
Further reading10min
1 exercício prático
Pulsars: test your understanding30min
4 horas para concluir
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 horas para concluir
7 vídeos (Total 35 mín.)
7 videos
Supermassive black holes3min
What is cross-matching?4min
Evaluating time complexity5min
A (much) faster algorithm6min
Module summary2min
Interview with Brendon Brewer8min
1 exercício prático
Supermassive black holes: test your understanding30min
4 horas para concluir
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 horas para concluir
7 vídeos (Total 35 mín.)
7 videos
Exoplanets4min
Querying database with SQL4min
More advanced SQL4min
Joining tables in SQL6min
Module summary2min
Interview with Jon Jenkins8min
1 exercício prático
Exoplanets - test your understanding30min
4 horas para concluir
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 horas para concluir
6 vídeos (Total 29 mín.)
6 videos
The lifecycle of stars6min
Setting up your own database5min
Exploring a star cluster4min
Module summary2min
Interview with Emily Petroff6min
1 exercício prático
Stars - test your understanding30min
Avaliações
Principais avaliações do DATA-DRIVEN ASTRONOMY
por SK10 de Set de 2020
Really amazing course! Gave me insights into how data analysis works in the field of astronomy and how one can use different machine learning techniques to classify the huge amounts of data generated.
por MC28 de Fev de 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
por TS14 de Dez de 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!
por GB21 de Abr de 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!
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