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.
ofrecido por
Data-driven Astronomy
Universidad de Sidney
Acerca de este Curso
30,094 vistas recientes
Resultados profesionales del estudiante
32%
comenzó una nueva carrera después de completar estos cursos
23%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Habilidades que obtendrás
Python ProgrammingMachine LearningApplied Machine LearningSQL
Resultados profesionales del estudiante
32%
comenzó una nueva carrera después de completar estos cursos
23%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 25 horas para completar
Sugerido: 6 weeks of study, 4-6 hours/week
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Universidad de Sidney
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.
Programa - Qué aprenderás en este curso
4 horas para completar
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 completar
8 videos (Total 30 minutos), 1 lectura, 4 cuestionarios
8 videos
Course overview2m
Pulsars3m
Diving in: imaging stacking5m
Challenge: the median doesn't scale2m
The solution: improving your method3m
Module summary1m
Interview with Aris Karastergiou6m
1 lectura
Further reading10m
1 ejercicio de práctica
Pulsars: test your understanding10m
4 horas para completar
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 completar
7 videos (Total 35 minutos)
7 videos
Supermassive black holes3m
What is cross-matching?4m
Evaluating time complexity5m
A (much) faster algorithm6m
Module summary2m
Interview with Brendon Brewer8m
1 ejercicio de práctica
Supermassive black holes: test your understanding10m
4 horas para completar
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 completar
7 videos (Total 35 minutos)
7 videos
Exoplanets4m
Querying database with SQL4m
More advanced SQL4m
Joining tables in SQL6m
Module summary2m
Interview with Jon Jenkins8m
1 ejercicio de práctica
Exoplanets - test your understanding10m
4 horas para completar
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 completar
6 videos (Total 29 minutos)
6 videos
The lifecycle of stars6m
Setting up your own database5m
Exploring a star cluster4m
Module summary2m
Interview with Emily Petroff6m
1 ejercicio de práctica
Stars - test your understanding10m
Revisiones
Principales revisiones sobre 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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
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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