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Unit of study_

OLET1620: Data Science in Astronomy: Analysis

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 learn how to manage your data with databases, and use the SQL language to ask questions about your data. You will also learn how to explore your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, and modern programming language used throughout astronomy. This will be run as a 0 cp + 2 cp unit of study. Students should have strong programming skills in Python 3, with a good understanding of loops, decisions and user-defined functions.


Academic unit Physics Academic Operations
Unit code OLET1620
Unit name Data Science in Astronomy: Analysis
Session, year
Semester 1, 2020
Attendance mode Online
Location Camperdown/Darlington, Sydney
Credit points 2

Enrolment rules

Assumed knowledge

Students should have strong programming skills in Python 3, with a good understanding of loops, decisions and user-defined functions.

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Henry Mitchell Poetrodjojo,
Tutor(s) James Leung ,
Type Description Weight Due Length
Final exam Final exam
Written exam - short answer
60% Formal exam period 1 hour
Outcomes assessed: LO1 LO4 LO3 LO2
Tutorial quiz 4 weekly quizzes
Online quizzes in Canvas.
10% Multiple weeks 5 multiple choice questions
Outcomes assessed: LO1
Assignment Week 1 tutorials
Write a working Python script
7.5% Week 03
Due date: 13 Mar 2020
Outcomes assessed: LO2 LO3 LO4
Assignment Week 2 tutorials
Write a working Python script
7.5% Week 06
Due date: 03 Apr 2020
Outcomes assessed: LO2 LO3 LO4
Assignment Week 3 tutorials
Write a working Python script
7.5% Week 09
Due date: 01 May 2020
Outcomes assessed: LO2 LO3 LO4
Assignment Week 4 tutorials
Write a working Python script
7.5% Week 12
Due date: 22 May 2020
Outcomes assessed: LO2 LO3 LO4
  • Quizzes – Quizzes are 5 multiple choice questions, designed to solidify your understanding of the current module. You must score at least 3/5 in order to pass
  • Tutorial Assignments – The tutorial assignments will put the knowledge you have learnt from the current module to the test. You will be required to write working Python scripts to complete the assignments
  • Final exam -  The final exam will consist of 4 short answer questions which will test not only your ability to write Python scripts, but your theory knowledge and problem solving skills.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100

At HD level, a student demonstrates a flair for the subject as well as a detailed and comprehensive understanding of the unit material. A “High Distinction” reflects an exceptional level of achievement and is awarded to a student who demonstrates their ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts. 


75 - 84

At D level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A “Distinction” reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a reasonably well developed critical analysis of theoretical concepts.


65 - 74

At CR level, a student demonstrates a good command and knowledge of the unit material. A “Credit” reflects solid achievement and is awarded to a student who has a broad general understanding of the unit material and can solve routine problems and/or identify and superficially discuss theoretical concepts.


50 - 64

At P level, a student demonstrates proficiency in the unit material. A “Pass” reflects satisfactory achievement and is awarded to a student who has threshold knowledge of the subject and can solve simple problems and can accuracy identify key theoretical concepts.


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Querying your data Online class (4 hr) LO1 LO2 LO3
Online tutorial Online class (6 hr) LO3
Week 02 Managing your data Online class (4 hr) LO1 LO2 LO3
Online tutorial Online class (6 hr) LO3
Week 03 Learning from data: regression Online class (4 hr) LO1 LO2 LO4
Online tutorial Online class (6 hr) LO4
Week 04 Learning from data: classification Online class (4 hr) LO1 LO2 LO4
Online tutorial Online class (6 hr) LO4

Attendance and class requirements

  • Attendance: Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where online tutorials/workshops/virtual laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. Penalties will not be applied if technical issues, etc. prevent attendance at a specific online class. In that case, students should discuss the problem with the coordinator, and attend another session, if available.
  • Final exam: Students must get at least 40% in the exam (and 50% overall) to pass this Unit of Study.

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 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

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.

At the completion of this unit, you should be able to:

  • LO1. demonstrate an understanding of some astronomical phenomena
  • LO2. discuss the type of problems that may arise when dealing with big data
  • LO3. demonstrate how to store and query data using SQL
  • LO4. write short programs (in Python) to use machine learning to analyse datasets.

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
No changes have been made since this unit was last offered.

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