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 Sydney
Informações sobre o curso
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Resultados de carreira do aprendiz
32%
comecei uma nova carreira após concluir estes cursos
23%
consegui um benefício significativo de carreira com este curso
Habilidades que você terá
Python ProgrammingMachine LearningApplied Machine LearningSQL
Resultados de carreira do aprendiz
32%
comecei uma nova carreira após concluir estes cursos
23%
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. 25 horas para completar
Sugerido: 6 weeks of study, 4-6 hours/week
Inglês
Legendas: Inglês
Programa - O que você aprenderá com este curso
4 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.
4 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 understanding10min
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 understanding10min
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 understanding10min
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 understanding10min
Avaliações
Principais avaliações do DATA-DRIVEN ASTRONOMY
por 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
por 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!
por 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!
por 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.
por 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.
por 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!
por 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.
por 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.
por 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.
por 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!
por 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.
por 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!
por RHMay 5, 2017
Great class! I didn't know that these techniques were used in astronomy. I am looking forward to more science and scientific computing courses from Coursera.
por SHFeb 25, 2018
One of the interesting course and topic of coursera. Very informative, even you are not from this field, you will get lots of knowledge of this Astronomy.
por XLMar 11, 2018
It's awesome! the combination of lecture and coding-tast is a very brilliant mode. This is my first course I have ever finished in Coursera! Thanks!
por EDSep 29, 2018
Excellent. Teaching is thorough and concise, the quizzes and external coding problems are challenging enough but not overwhelming. A great course.
por MWMar 7, 2019
This is a very well organized, interesting, and, quite frankly, fun class. I especially liked being able to do hands-on exercises in Python.
por APApr 23, 2017
Excellent course for astronomy enthusiasts with really useful and informative Python Assignments. One of the best MOOCs I have attended.
por JIAug 30, 2019
I enjoyed the course.\n\nThe only issue was with a couple of the python labs not having the data to try within my own environment.
por PRJun 16, 2019
It's a good start point if we want to understand actual astronomy and the analysis of new big surveys like SDSS or the next LSST
oferecido por
Universidade de Sydney
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao adquirir o Certificado?
Quando você adquire o Certificado, ganha acesso a todo o material do curso, incluindo avaliações com nota atribuída. Após concluir o curso, seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
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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