Outline

Overview

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

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))
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Manisha Caleb, manisha.caleb@sydney.edu.au
Lecturer(s)	Elaine Sadler, elaine.sadler@sydney.edu.au
Lecturer(s)	Daniel Huber, daniel.huber@sydney.edu.au

Assessment

The census date for this unit availability is 2 September 2024

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Final exam	40%	Formal exam period	2 hours
Outcomes assessed:
Small test	Mid-semester test Mid-semester test	30%	Week 07 Due date: 09 Sep 2024 at 12:00	1 hour
Outcomes assessed:
Small continuous assessment	Lab checkpoints Lab checkpoints	30%	Weekly	Lab time + 0-1 additional hours per lab
Outcomes assessed:
= hurdle task ?

Assessment summary

Weekly Lab checkpoints: In the lab tutorials you will write code to analyse data based on the scientific concepts covered in the lectorials. The lab checkpoints each week will be based on the lectorials taught during that week and are to be submitted at the end of every week.

Mid-semester test: This is a closed book, supervised exam in which you will work through a Jupyter notebook and submit at conclusion of test. It will be based on the material covered during Weeks 1 - 6 and will be held on the Monday of Week 7. More details will be provided closer to the date.

Final Exam: The final exam will test you knowledge of all the material covered from Weeks 1 - 12. Must be attempted and must meet hurdle mark threshold otherwise = AF grade. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Weekly lab checkpoints: Work through a Jupyter notebook and submit two lab checkpoints per lab. The best 40 checkpoints (out of 46) will count towards the final mark for this course component.

Mid-semester test: Supervised closed book test in which you will work through a Jupyter notebook and submit at conclusion of test.

Final exam: Supervised closed book exam, conducted on a computer. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Data Science in Astronomy	Lecture and tutorial (1 hr)
	Introduction to Data Science in Astronomy	Computer laboratory (1 hr)
	Introduction to Data Science in Astronomy	Lecture and tutorial (1 hr)
	Introduction to Data Science in Astronomy	Computer laboratory (2 hr)
Week 02	Introduction to Data Science in Astronomy	Lecture and tutorial (1 hr)
	Introduction to Data Science in Astronomy	Computer laboratory (1 hr)
	Introduction to Data Science in Astronomy	Lecture and tutorial (1 hr)
	Introduction to Data Science in Astronomy	Computer laboratory (2 hr)
Week 03	Cross-matching Part 1	Lecture and tutorial (1 hr)
	Cross-matching Part 1	Computer laboratory (1 hr)
	Cross-matching Part 1	Lecture and tutorial (1 hr)
	Cross-matching Part 1	Computer laboratory (2 hr)
Week 04	Cross-matching Part 2	Lecture and tutorial (1 hr)
	Cross-matching Part 2	Computer laboratory (1 hr)
	Cross-matching Part 2	Lecture and tutorial (1 hr)
	Cross-matching Part 2	Computer laboratory (2 hr)
Week 05	Databases Part 1	Lecture and tutorial (1 hr)
	Databases Part 1	Computer laboratory (1 hr)
	Databases Part 1	Lecture and tutorial (1 hr)
	Databases Part 1	Computer laboratory (2 hr)
Week 06	Databases Part 2	Lecture and tutorial (1 hr)
	Databases Part 2	Computer laboratory (1 hr)
	Databases Part 2	Lecture and tutorial (1 hr)
	Databases Part 2	Computer laboratory (2 hr)
Week 07	Supervised Machine Learning Part 1: Decision Trees	Lecture and tutorial (1 hr)
	Supervised Machine Learning Part 1: Decision Trees	Computer laboratory (1 hr)
	Supervised Machine Learning Part 1: Decision Trees	Lecture and tutorial (1 hr)
	Supervised Machine Learning Part 1: Decision Trees	Computer laboratory (2 hr)
Week 08	Supervised Machine Learning Part 2: Random Forest	Lecture and tutorial (1 hr)
	Supervised Machine Learning Part 2: Random Forest	Computer laboratory (1 hr)
	Supervised Machine Learning Part 2: Random Forest	Lecture and tutorial (1 hr)
	Supervised Machine Learning Part 2: Random Forest	Computer laboratory (2 hr)
Week 09	Supervised Machine Learning Part 3: Neural Networks	Lecture and tutorial (1 hr)
	Supervised Machine Learning Part 3: Neural Networks	Computer laboratory (1 hr)
	Supervised Machine Learning Part 3: Neural Networks	Lecture and tutorial (1 hr)
	Supervised Machine Learning Part 3: Neural Networks	Computer laboratory (2 hr)
Week 10	Unsupervised Machine Learning Part 1: Clustering	Lecture and tutorial (1 hr)
Week 10	Unsupervised Machine Learning Part 1: Clustering	Computer laboratory (2 hr)
Week 11	Unsupervised Machine Learning Part 2: Anomaly detection	Lecture and tutorial (1 hr)
	Unsupervised Machine Learning Part 2: Anomaly detection	Computer laboratory (1 hr)
	Unsupervised Machine Learning Part 2: Anomaly detection	Lecture and tutorial (1 hr)
	Unsupervised Machine Learning Part 2: Anomaly detection	Computer laboratory (2 hr)
Week 12	Astrostatistics	Lecture and tutorial (1 hr)
	Astrostatistics	Computer laboratory (1 hr)
	Astrostatistics	Lecture and tutorial (1 hr)
	Astrostatistics	Computer laboratory (2 hr)
Week 13	Wrap-up	Lecture and tutorial (5 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

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

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

New unit

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

PHYS2914: Data Science in Astronomy (Advanced)

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect