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Unit of study_
PHYS2914: Data Science in Astronomy (Advanced)
2027 unit information
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 astronomical datasets. You will learn about different types of data in astronomy and how to analyze and visualise this data, how to implement algorithms that work and how to think about scaling these algorithms to large datasets. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Undergraduate |
|---|---|
| Academic unit | Physics Academic Operations |
| Credit points | 6 |
|
Prerequisites:
?
|
65 or above in (PHYS1003 or PHYS1004 or PHYS1902 or PHYS1904) and 65 or above in (PHYS1001 or PHYS1002 or PHYS1901 or PHYS1903) |
|---|---|
|
Corequisites:
?
|
None |
| Prohibitions:
?
|
PHYS2014 |
| Assumed knowledge:
?
|
((MATH1X21 or MATH1931 or MATH1X01 or MATH1906 or MATH1011) and (MATH1X02) and (MATH1X23 or MATH1933 or MATH1X03 or MATH1907 or MATH1013) and (MATH1X04 or MATH1X05)) or ((MATH1061 or MATH1961 or MATH1971) and (MATH1062 or MATH1962 or MATH1972)) |
At the completion of this unit, you should be able to:
- LO1. Identify physical processes leading to observed astronomical phenomena
- LO2. Classify problems that may arise when dealing with big data in astronomy, and implement solutions to these problems
- LO3. Implement a data processing pipeline, including storing, querying and visualising astronomical data
- LO4. Use Python to manipulate large astronomical datasets, perform statistical analyses and assess the reliability of the results and the connection of the results to the underlying science.
- LO5. Implement machine learning techniques involving astronomical data and judge the quality of the results
- LO6. Identify and justify when and why “big data” analysis techniques are needed in astronomy, and how the analysis of results helps further our knowledge of the universe
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
|
Semester 2 2027
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
|
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Modes of attendance (MoA)
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