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Data-driven Astronomy

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.

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Rating 4.8 based on 170 ratings
Length 7 weeks
Effort 6 weeks of study, 4-6 hours/week
Starts Jun 26 (43 weeks ago)
Cost $49
From The University of Sydney via Coursera
Instructors Tara Murphy, Simon Murphy
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Engineering Science
Tags Data Science Data Analysis Physical Science And Engineering Physics And Astronomy

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What people are saying

machine learning

A great course for a good understanding of the tools and the theoretical requisites of data driven astronomy Great course to get an overview of the type of problems that astronomy faces and how to use data and machine learning techniques to solve them.

Note that the exercises use the Python programming language (no substitutes permitted), fairly generic Structured Query Language (SQL) for databases, numpy (science math tools) Python library, scikit-learn (machine learning) Python library, and matplotlib (math & plotting) Python library.

Especially if you already use Python a bit and want to try out some machine learning and other astronomy related python tools.

I wanted to learn something about astronomy and to play with the data - the cross-matching and machine learning were my favourite parts of the course.

If you're curious about Deep Learning, like I am, and are an aspirant in the field of Machine Learning, I highly suggest this course if you're trying to work your way around beginning your journey in Python.

That was the course I was looking for, after taking a course on Machine Learning by Andrea NG, from Stanford University.

I learned a lot about astronomy, quasars, galaxies, machine learning, python and the scientific libraries numpy, scikit-learn, scipy, creating programs that are optimised in memory use and that complete in reasonable time Unique course, very much excited to learn new things Most of all courses in astronomy and astrophysics are just introduction to subject or provides little advanced theoretical perspective but this is one of the courses which teach us practical astronomy and let us have insights of how astronomers really use physics as well as computers to to get something good out of it.

Machine learning was new for me, specifically decision trees.

As addition some introduction in machine learning is provided.

It has really helped in matching my interests in Astrophysics and Machine Learning.

If you are instead a novice with regards to coding, additional parallel effort may be required, but the course contents will guide you well in the endeavor.One aspect of the course that may be specially challenging is the relatively speedy run through the theory and concepts of Machine Learning.

Fue un excelente curso , me ayuda mucho para poder intentar el procesado de unos catalogos de galaxias en especial la parte del machine learning aplicado This is a very well organized, interesting, and, quite frankly, fun class.

What I liked about the course was the graded programming assignments, which help to introduce a person to machine learning techniques and other problems in astronomy data processing.

Anyone who wants to learn about the application of Machine Learning in the field of Astronomy , this course is a must.

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really enjoyed this course

I really enjoyed this course.

I've really enjoyed this course!

I really enjoyed this course and I would recommend it to any one with an interest in this or related subjects.

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highly recommend

Highly recommended course for both astronomical and computer science students.

I highly recommend this course, even if you are already experienced in a subset of the above.

I highly recommend it.

Highly recommended!

Highly recommend it anyone curious about Universe and to understand it better!

Overall the course is great, I highly recommend it to astronomers who want to learn some new tricks in data science.

For anyone intrested in Astrostats & Astroinformatics, highly recommended!

Highly recommended.

I highly recommend.

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data analysis

The teachers use a wide range of astronomical subjects to illustrate the different techniques used in data analysis.

I would recommend it to everyone interested in data analysis in modern science.

What a perfect blend of real astronomy, programming, Python, SQL, machine-learning, and data analysis.

Really good basic intro to data analysis, particularly applying this to the astronomical domain.

I am a computer scientist by profession, and came here to learn how astronomers perceive data analysis software in their pursuit.

This course not only introduced me to how software is used for data analysis in astronomy, but also gave me insights on challenges the community is (or could potentially be) facing.

Me ha abierto la curiosidad por investigar Great course and a great opportunity to learn about data analysis, classification of galaxies and astronomy.

This is a very interesting introduction to data analysis and machine learning for astronomy.

A really good course covering a variety of subjects in both astronomy and data analysis which is exactly the combination I was looking for.

Excellent course both on data analysis and astronomy !

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big data

Good introduction course to big data.

Course follows a very intuitive approach to impart modern astronomy knowledge with big data as a backdrop.

Operating a FITS file, image stacking, image processing, DBMS, SQL query, editing database, reading database, taking output from a big database using classifiers, finding distance to stars and much more.

Excellent introduction into the astronomical exploration using the methods of big data 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.

Excellent course taking us into the realms of big data, computational methods even database and SQL, I enjoyed it very much.

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recommend it

I recommend it.

I've learnt a lot of things and I would recommend it to everyone First, I enjoyed the course, thank you.

new things

There are a lot of new things to learn in it.

I had fun while learning new things, and I hope to see a sequel to such a novel course.

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Rating 4.8 based on 170 ratings
Length 7 weeks
Effort 6 weeks of study, 4-6 hours/week
Starts Jun 26 (43 weeks ago)
Cost $49
From The University of Sydney via Coursera
Instructors Tara Murphy, Simon Murphy
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Engineering Science
Tags Data Science Data Analysis Physical Science And Engineering Physics And Astronomy

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