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The University of Sydney - Data-driven Astronomy 

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Data-driven Astronomy
 at 
Coursera 
Overview

Equip with the necessary skills to become proficient in data-driven astronomy research and contribute to the advancement of the field

Duration

23 hours

Start from

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Total fee

Free

Mode of learning

Online

Difficulty level

Intermediate

Official Website

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Credential

Certificate

Data-driven Astronomy
 at 
Coursera 
Highlights

  • Earn a certificate after completion of the course
  • Financial aid facility available
  • Projects and assignments for better preparation
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Data-driven Astronomy
 at 
Coursera 
Course details

Skills you will learn
Who should do this course?

Undergraduate and Graduate Students in Astronomy and Astrophysics

Data Scientists and Analysts

Professional Astronomers and Astrophysicists

What are the course deliverables?

Understand the key challenges and opportunities in data-driven astronomy

Apply data science techniques to analyze and interpret astronomical data

Use computational tools to manage and visualize large datasets

Develop machine learning models for specific astronomical applications

Critically evaluate the ethical implications of data usage in astronomy

 

More about this course

The course aims to equip students with the skills to analyze, interpret, and draw meaningful insights from large and complex datasets that are common in modern astronomy

This course introduces students to the principles and practices of data science within the context of astronomy

Students will learn to harness the power of large astronomical datasets to explore the universe and answer fundamental questions about its origin, structure, and evolution

Data-driven Astronomy
 at 
Coursera 
Curriculum

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

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Data-driven Astronomy
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Coursera 
Faculty details

Tara Murphy
Tara Murphy is an astrophysicist working in the School of Physics at the University of Sydney. She is a Australian Research Council Future Fellow and a Chief Investigator in the ARC Centre of Excellence for All Sky Astrophysics. Her research focuses on detecting and studying transient and highly variable astrophysical phenomena with next generation radio telescopes.
Simon Murphy
I work with data from the Kepler Space Telescope to study the interior structure of stars. This involves observations of stellar oscillations using a technique called asteroseismology - analogous to the use of seismic activity on Earth to learn about its interior. I also have a keen interest in finding new binary stars and planets, and using them to advance our knowledge of star and planet formation. Outside of work, I enjoy cycling, soccer and board games.

Data-driven Astronomy
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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