Data-driven Astronomy from Coursera

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.

What's inside

Syllabus

Thinking about data

This module introduces the idea of computational thinking, and how big data can make simple problems quite challenging to solve. We use the example of calculating the median and mean stack of a set of radio astronomy images to illustrate some of the issues you encounter when working with large datasets.

Big data makes things slow

In this module we explore the idea of scaling your code. Some algorithms scale well as your dataset increases, but others become impossibly slow. We look at some of the reason for this, and use the example of cross-matching astronomical catalogues to demonstrate what kind of improvements you can make.

Querying your data

Most large astronomy projects use databases to manage their data. In this module we introduce SQL - the language most commonly used to query databases. We use SQL to query the NASA Exoplanet database and investigate the habitability of planets in other solar systems.

Managing your data

This module introduces the basic principles of setting up databases. We look at how to set up new tables, and then how to combine Python and SQL to get the best out of both approaches. We use these tools to explore the life of stars in a stellar cluster.

Learning from data: regression

This module introduces the idea of machine learning. We look at standard methodology for running machine learning experiments, and then apply this to calculating redshifts of distant galaxies using decision trees for regression.

Learning from data: classification

In this final module we explore the limitations of decision tree classifiers. We then look at ensemble classifiers, using the random forest algorithm to classify images of galaxies into different types.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops computational thinking, which is standard in science

Taught by Tara Murphy and Simon Murphy, who are recognized for their work in astronomy

Teaches data analysis skills using Python 3, which is widely used in astronomy

Examines cutting-edge topics in astronomy, including black holes and exoplanets

Requires some prior knowledge of Python, which may be a barrier for complete beginners

Assumes familiarity with databases, which may require additional learning for some students

Reviews summary

Data-driven astronomy

learners say this course is largely positive with engaging assignments and a highly structured curriculum. The course delves into astronomy from a data-driven perspective, utilizing real-world data analysis, programming, and machine learning algorithms commonly used in astronomy.

The course covers a variety of astronomy topics, including exoplanets, galaxy morphology, and pulsars.

"Exceptional course. From a computer science point of view provides a very good example of scientific application of several topics (complexity, search algorithms, databases, practical Python and SQL exercises, machine learning...)."

"If your interest on astronomical data science then this course for you."

"Data-driven astronomy is a useful course about necessary instruments in modern science and work in general. If you want to learn about Python, SQL and machine learning - this course is for you!"

The course is well-organized and provides a clear learning path.

"The course has been excellently structured for online learning."

"Very well designed course. Each week/module introduced a specific astronomical question, and then introduced the data analysis tools to answer the question. This allowed the learner to become familiar both with the question and the tool."

"The Grok platform was also very helpful."

This course provides hands-on experience with Python, SQL, and machine learning algorithms.

"great for learning basic stuff regarding data management and machine learning algotithms"

"A great course that demonstrates how data science is applied to astronomy."

"This course is very fun to work on. I like the platform they are using to practice coding. The videos are also very informative and interesting."

The course emphasizes hands-on practice through coding assignments and exercises using real-world astronomical data.

"The course, video, practice, all are really good."

"Great Course! The practice section on Grok platform is what makes it especially good, considering you don’t really need to setup a programming environment on your own machine."

"Very practical and hands on. "

The course may not be comprehensive enough for advanced learners and does not provide background information on all covered topics.

"If you already have some programming knowledge, esp in SQL or Python, the practical assignments you'll likely find quite easy and can be completed in under half the estimated time."

"This course pretty much restored my faith in the online courses. Hands down, this is the best online course I have ever had, not only in astronomy. This course had everything to satisfy my needs: I had an opportunity to analyse actual astronomical data, which made me feel like a small child againI have learned to program in python for scientific reasons, which I previously only used for the backend purposes. The python skills also helped my in my computer modelling studies at the university.*Also, even though I am working with SQL pretty much every day, this course still managed to teach me some new concepts, and understand joins better. This course, even that it's on the astronomy, managed to explain SQL much better than the conventional lectures.Regarding the lecturers, both lecturers seemed extremely passionate in what they are talking about, and also managed to explain everything in very concise manner.Also, I really enjoyed the quizzes, since they do not directly ask what was mentioned in the video, but the questions are twisted a bit to make you actually think for answer.Finally, this course presents a lot of further research to investigate yourself. This ranges from scientific papers to the links to classify galaxies."

"The course is well defined with clear presentation. From an astronomy prespective, the course provides necessary knowledge for inspiring us to step into data analysis and machine learning. Thank you University of Sidney to host such a course on astronomy"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data-driven Astronomy with these activities:

Review machine learning with Python

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Review the fundamentals of machine learning and Python, which will help in understanding the upcoming course material.

Browse courses on Machine Learning

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Go over the basics of machine learning, including supervised and unsupervised learning.
Set up a Python environment
Practice implementing machine learning algorithms in Python

Practice solving data structures problems

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Solving data structures problems will improve your problem-solving skills and prepare you for the course's hands-on exercises.

Browse courses on Data Structures

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Find a list of data structures problems
Solve the problems using Python
Review your solutions and identify areas for improvement

Read Big Data: A Revolution That Will Transform How We Live, Work, and Think

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This book provides a comprehensive overview of big data and its implications for society.

View Reinventing Capitalism in the Age of Big Data on Amazon

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Read the book
Summarize each chapter

Eight other activities

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Practice implementing algorithms in Python

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Implementing algorithms in Python will help you develop the computational thinking skills necessary for this course.

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Choose an algorithm to implement
Code the algorithm in Python
Test your algorithm on sample data
Refine your implementation based on testing results

Follow a tutorial on using Python for astronomy

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Following a tutorial will help you get started with using Python for astronomy.

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Find a tutorial on using Python for astronomy
Follow the tutorial and complete the exercises

Join a study group for the course

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Joining a study group will provide you with opportunities to discuss the course material with other students and get help from your peers.

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Find a study group for the course
Attend study group meetings
Participate in discussions

Create a data visualization of astronomical data

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Creating a data visualization of astronomical data will help you learn how to communicate your findings effectively.

Browse courses on Data Visualization

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Choose a dataset to visualize
Select a data visualization tool
Create a data visualization
Refine your visualization based on feedback

Practice working with large datasets

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Working with a large dataset will give you hands-on experience and help you develop the skills needed for the course.

Browse courses on Big Data

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Download a large dataset from a public source
Clean and prepare the data
Analyze the data using Python

Attend a workshop on Python for astronomy

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Attending a workshop on Python for astronomy will give you the opportunity to learn from experts and ask questions.

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Find a workshop on Python for astronomy
Attend the workshop
Ask questions and participate in discussions

Create a presentation on a topic related to astronomy

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Creating a presentation on a topic related to astronomy will help you deepen your understanding of the material and improve your communication skills.

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Research the topic
Choose a topic related to astronomy
Create a presentation using a tool like PowerPoint or Google Slides
Practice delivering your presentation
Present your presentation to your classmates or a group of friends

Develop a data visualization for a dataset related to astronomy

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Creating a data visualization for a dataset related to astronomy will help you develop your data analysis and visualization skills.

Show steps

Choose a dataset related to astronomy
Clean and prepare the data
Choose a data visualization tool, such as Tableau or D3
Create a data visualization

Career center

Learners who complete Data-driven Astronomy will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. Astronomy is a field that has adopted machine learning technologies to enhance data analysis. Machine learning is one of the key topics covered in data-driven astronomy. This course would be of particular interest to someone interested in working in this role.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, interpret, and present data. They develop and apply statistical methods to a wide range of problems in various fields. Astronomy is just one example of a field that relies heavily on statisticians to interpret large datasets. The skills learned in data-driven astronomy may be helpful to students interested in careers as a Statistician or in data science.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course in data-driven astronomy provides a solid foundation in some of the core competencies of a successful Data Scientist, including data analysis, data mining, and machine learning with applications and examples directly related to astronomy.

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Data Architect

Data Architects are responsible for designing, building, and maintaining data systems. They work with data scientists and other stakeholders to ensure that data is accessible, reliable, and secure. Data-driven astronomy provides a comprehensive overview of data analysis and management techniques, making it a suitable choice for someone looking to become a Data Architect. The skills learned in this course could be applied to astronomy and other fields.

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Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. Astronomy is one of many fields that rely heavily upon data analysis to advance understanding. Data-driven astronomy provides a foundation in the fundamentals of computational thinking, advanced algorithms, and machine learning tools. The skills learned in this course can be applied to a variety of in-demand data analysis roles.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They are employed in a variety of industries, including finance, insurance, and consulting. The skills learned in this data-driven astronomy course, including computational thinking, data analysis, and machine learning, are in high demand for roles such as Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines. They work with data scientists and other stakeholders to ensure that data is available, clean, and ready for analysis. This course in data-driven astronomy provides a solid foundation in data analysis and management techniques, making it a suitable choice for someone looking to become a Data Engineer. The skills learned in this course could be applied to astronomy and other fields.

See salaries and explore the career path for Data Engineer

Database Administrator

Database Administrators are responsible for managing and maintaining databases. They ensure that data is stored securely, efficiently, and reliably. The data management topics covered in this data-driven astronomy course would be helpful to someone looking to become a Database Administrator.

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Research Scientist

Research Scientists conduct scientific research in a variety of fields, including astronomy, biology, chemistry, and physics. This course may be helpful to someone interested in a career as a Research Scientist as it provides a strong foundation in data analysis, data mining, and machine learning techniques. These skills are applicable to a variety of roles in research, including data science and scientific computing.

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Business Analyst

Business Analysts use data to identify and solve business problems. They work with stakeholders to gather requirements, analyze data, and develop solutions. The skills learned in this data-driven astronomy course, including data analysis and machine learning, may be helpful to someone looking to become a Business Analyst.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be helpful to someone interested in roles such as Software Engineer as it covers core topics including algorithms, data management, and machine learning. The skills learned in this course could be applied to a variety of specializations within software engineering, including data engineering and scientific computing roles.

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Product Manager

Product Managers are responsible for developing and managing products. They work with engineers, designers, and other stakeholders to ensure that products meet the needs of customers. The skills learned in this data-driven astronomy course, such as data analysis and machine learning, may be helpful to someone looking to become a Product Manager.

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Management Consultant

Management Consultants help organizations improve their performance. They use data to identify problems and develop solutions. The skills learned in this data-driven astronomy course, including data analysis and machine learning, may be helpful to someone looking to become a Management Consultant.

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Astronomer

Astronomers use telescopes to observe celestial objects and collect data about their properties. Analyzing raw data requires a solid understanding of statistical methods and algorithms. Data-driven astronomy provides a practical foundation in some of the necessary skills, such as organizing, processing, and analyzing this data.

See salaries and explore the career path for Astronomer

Astrophysicist

An Astrophysicist is concerned with the physical properties of the universe, stars, and other celestial bodies. Investigation frequently involves analyzing large data sets to look for patterns. Statisticians are often responsible for designing and executing these data analysis programs and algorithms. This course on data-driven astronomy might be useful to an Astrophysicist as it provides a solid overview of some of the data science skills used in the field.

See salaries and explore the career path for Astrophysicist