Acerca de este curso: 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. Regardless of whether you’re already a scientist, studying to become one, or just interested in how modern astronomy works ‘under the bonnet’, this course will help you explore astronomy: from planets, to pulsars to black holes. Course outline: Week 1: Thinking about data - Principles of computational thinking - Discovering pulsars in radio images Week 2: Big data makes things slow - How to work out the time complexity of algorithms - Exploring the black holes at the centres of massive galaxies Week 3: Querying data using SQL - How to use databases to analyse your data - Investigating exoplanets in other solar systems Week 4: Managing your data - How to set up databases to manage your data - Exploring the lifecycle of stars in our Galaxy Week 5: Learning from data: regression - Using machine learning tools to investigate your data - Calculating the redshifts of distant galaxies Week 6: Learning from data: classification - Using machine learning tools to classify your data - Investigating different types of galaxies Each week will also have an interview with a data-driven astronomy expert. Note that some knowledge of Python is assumed, including variables, control structures, data structures, functions, and working with files.
Para quién es esta clase: This course is aimed at science students with an interest in computational approaches to problem solving, people with an interest in astronomy who would like to learn current research methods, or people who would like to improve their programming by applying it to astronomy examples.
Enseñado por: Tara Murphy, Associate Professor
School of PhysicsEnseñado por: Simon Murphy, Postdoctoral Researcher
School of Physics
| Nivel | Intermedio |
| Compromiso | 6 weeks of study, 4-6 hours/week |
| Idioma | English |
| Requerimientos de hardware | You'll need to have a computer with internet access. |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
- Discussion Prompt: Introduce yourself (optional)
- Vídeo: Course overview
- Vídeo: Pulsars
- Vídeo: Diving in: imaging stacking
- Vídeo: Challenge: the median doesn't scale
- Discussion Prompt: How could you improve the algorithm?
- Vídeo: The solution: improving your method
- LTI Item: Calculating the median stack
- Vídeo: Module summary
- Reading: Further reading
- Vídeo: Interview with Aris Karastergiou
- Discussion Prompt: What scaling problems have you encountered?
- Vídeo: Supermassive black holes
- Vídeo: What is cross-matching?
- Vídeo: Evaluating time complexity
- Vídeo: A (much) faster algorithm
- LTI Item: Crossmatching with k-d trees
- Vídeo: Module summary
- Vídeo: Interview with Brendon Brewer
- Discussion Prompt: Do you use databases in your work?
- Vídeo: Exoplanets
- Vídeo: Querying database with SQL
- Vídeo: More advanced SQL
- Vídeo: Joining tables in SQL
- LTI Item: Joining tables with SQL
- Vídeo: Module summary
- Vídeo: Interview with Jon Jenkins
- Vídeo: The lifecycle of stars
- Vídeo: Setting up your own database
- Vídeo: Exploring a star cluster
- LTI Item: Combining SQL and Python
- Vídeo: Module summary
- Vídeo: Interview with Emily Petroff
- Vídeo: The cosmological distance scale
- Vídeo: What is machine learning?
- Vídeo: Decision tree classifiers
- Vídeo: Estimating redshifts using regression
- LTI Item: Improving and evaluating our classifier
- Vídeo: Summary
- Vídeo: Interview with Ashish Mahabal
- Vídeo: Types of galaxies
- Vídeo: Morphological classification of galaxies
- Vídeo: Limitations of decision tree classifiers
- Reading: Classify some galaxies by hand!
- Discussion Prompt: Reflection on galaxy classification
- Vídeo: Improving our results with ensemble classifiers
- Vídeo: Module summary
- Vídeo: Interview with Karen Masters
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Overall the course is great, I highly recommend it to astronomers who want to learn some new tricks in data science. Meanwhile, it is not so useful for everyone else - lections are super brief and superficial, coding assignments are very easy and not challenging at all, not only you have a pretty self-explanatory template (do A, do B), but it is possible to view a complete solution right away, which is rather strange approach.
I've really enjoyed this course! There are a lot of new things to learn in it. Just to say something not so good from my point of view is that in the exercises sometimes the libraries or code to use could be a bit more explained or documented. But in general it's very good. I recommend it.
Excellent. Teaching is thorough and concise, the quizzes and external coding problems are challenging enough but not overwhelming. A great course.
I finished this course today. If you want to learn advanced concepts like machine learning, decision tree classification, SQL, and more; then this is the course for you! I'm a senior in high school, and I'm going to major in Astrophysics. If you love Computer Science this will be an interesting course, as it will show the applications of CS to Astronomy.