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Data-driven Astronomy

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20 hours
English
The University of Sydney
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CourseraCourses from Coursera
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For Rs. 2182
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

The course is from one of the top universities of the world - The University of Sydney.

Price

This course costs very less.

Difficulty

The students of this course have found this course easy.

Content

The students of this course have liked the content of this course.

Assignments

The students of this course have liked the assignments of this course.

Teaching

The students of this course have liked how the instructor has taught this course.

Satisfaction

The students of this course are overall satisfied with this course.

Edvicer's Rewards

You can get a cashback of ₹ 100 on buying this course.

Why should you choose this course?

Description

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.

Syllabus

Thinking about data
Big data makes things slow
Querying your data
Managing your data
Learning from data: regression
Learning from data: classification

What others say about this course

Reviews from Coursera

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, an  Read More ...

I have been an astronomy addict since I was a teenager; but, thinking about money, I had a computer science background. Now that I found out the wonderful universe of data science and specially its con  Read More ...

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.

Dr Tara Murphy is exceptionally good at extracting and compressing essential informations and transporting it to the audience. A very well structured course with phantastically produced short movies ab  Read More ...

First few weeks are challenging, from the coding point of view, but the knowledge that one gains about our Universe is simply fantastic. I've never enjoyed using a Programming language to solve, even t  Read More ...

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