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Data-driven Astronomy

Approx. 24 hours to complete

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Course Summary

Learn how to use data-driven techniques to analyze astronomical data and solve complex problems in astronomy.

Key Learning Points

Use Python and SQL to analyze astronomical data
Learn how to work with large astronomical datasets
Gain the skills needed to solve complex problems in astronomy

Job Positions & Salaries of people who have taken this course might have

- USA: $113,309
- India: ₹1,139,597
- Spain: €40,000
- USA: $113,309
- India: ₹1,139,597
- Spain: €40,000
- USA: $99,000
- India: ₹1,000,000
- Spain: €33,000
- USA: $113,309
- India: ₹1,139,597
- Spain: €40,000
- USA: $99,000
- India: ₹1,000,000
- Spain: €33,000
- USA: $83,000
- India: ₹840,000
- Spain: €29,000

Learning Outcomes

Analyze astronomical data using Python and SQL
Work with large datasets and solve complex astronomy problems
Apply data-driven techniques to astronomy research

Prerequisites or good to have knowledge before taking this course

Basic understanding of Python programming
Familiarity with fundamental astronomy concepts

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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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.

Outline

Thinking about data
Thinking about data
Course overview
Pulsars
Diving in: imaging stacking
Challenge: the median doesn't scale
The solution: improving your method
Module summary
Interview with Aris Karastergiou
Further reading
Pulsars: test your understanding

Big data makes things slow
Big data makes things slow
Supermassive black holes
What is cross-matching?
Evaluating time complexity
A (much) faster algorithm
Module summary
Interview with Brendon Brewer
Supermassive black holes: test your understanding

Querying your data
Organising your data
Exoplanets
Querying database with SQL
More advanced SQL
Joining tables in SQL
Module summary
Interview with Jon Jenkins
Exoplanets - test your understanding

Managing your data
Managing your big datasets
The lifecycle of stars
Setting up your own database
Exploring a star cluster
Module summary
Interview with Emily Petroff
Stars - test your understanding

Learning from data: regression
Learning from data
The cosmological distance scale
What is machine learning?
Decision tree classifiers
Estimating redshifts using regression
Summary
Interview with Ashish Mahabal
Cosmological distances - test your understanding

Learning from data: classification
Classifying your data
Types of galaxies
Morphological classification of galaxies
Limitations of decision tree classifiers
Improving our results with ensemble classifiers
Module summary
Interview with Karen Masters
Classify some galaxies by hand!
Galaxies - test your understanding

Summary of User Reviews

Data-Driven Astronomy is a highly rated course that teaches students how to analyze astronomical data. Many users found the course to be a valuable learning experience that helped them develop key skills.

Key Aspect Users Liked About This Course

The course provides students with hands-on experience working with real astronomical data, which many users found to be a highlight of the course.

Pros from User Reviews

Hands-on experience working with real astronomical data
Well-structured and easy to follow lessons
Engaging and knowledgeable instructors
Comprehensive coverage of key topics
Suitable for students with varying levels of experience

Cons from User Reviews

Some users found the course to be too basic
Lack of interaction with other students
No opportunity to ask questions in real-time
Some users found the course to be too focused on programming
Not suitable for students who prefer lecture-style learning

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Data-driven Astronomy

Science is undergoing a data explosion, and astronomy is leading the way. To analyse this data scientists need to be able to think computationally to solve problems....

Save Course