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.

Traffic lights

Read about what's good

what should give you pause

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

Applying data science to astronomy

According to learners, this course offers an excellent and engaging experience, blending practical data science techniques (Python, SQL, ML) with real astronomical datasets. Reviewers highlight the hands-on labs and coding exercises as a major strength, providing practical application and a solid foundation for data-driven research. However, a significant point raised is that the stated Python prerequisite ('some knowledge') is understated, with assignments often proving very difficult and requiring significant debugging skills for those with only basic coding experience. The course is highly recommended for those with a stronger technical background interested in science data.

Coding tasks are difficult but rewarding.

"Be prepared to spend time on the coding exercises; they are challenging but very rewarding."

"Assignments are definitely not trivial and require debugging skills."

"The coding assignments are where the real learning happens, but be prepared for a steep curve..."

"Interesting topics, but challenging implementation... the jump to the coding assignments feels large."

Support from staff and forum.

"The instructors were knowledgeable, and the forum community was helpful when I got stuck."

Covers SQL, ML, and algorithms well.

"I particularly enjoyed the machine learning modules and seeing how these techniques are used in real astronomical research."

"The SQL section was clear and practical."

"The explanations of complex topics like algorithm time complexity and different ML models were surprisingly clear."

"Good introduction to data handling and analysis in a scientific context."

Hands-on coding with real data.

"The way they integrated astronomy concepts with data science techniques made the learning process fascinating. The hands-on labs were excellent..."

"Absolutely brilliant course! The combination of practical Python, SQL, and ML applied to real astronomical datasets is exactly what I was looking for."

"Fantastic learning experience! The hands-on approach with real data is a major strength."

"The perfect blend of theory and practice. The astronomy context makes the technical parts much more intuitive and engaging."

Requires stronger Python than assumed.

"The level of Python required is significantly higher than 'some knowledge'. I struggled immensely with the coding assignments..."

"I have minimal Python experience, thinking 'some knowledge' meant basic scripting... Felt like a course for intermediate Python users, not beginners."

"Assumes more Python than just 'some knowledge' - beginners will struggle significantly with the assignments."

"Be prepared for a steep curve if your Python isn't strong."

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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Show all 11 activities

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.

Show steps

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Architect

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

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

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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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.

See salaries and explore the career path for Software Engineer

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

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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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.

See salaries and explore the career path for Product Manager

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