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Unit outline_

OLET1620: Data Science in Astronomy: Analysis

Intensive April - May, 2021 [Online] - Camperdown/Darlington, Sydney

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.

Unit details and rules

Academic unit Physics Academic Operations
Credit points 2
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
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

Yes

Teaching staff

Coordinator Jesse Van de Sande, jesse.vandesande@sydney.edu.au
Type Description Weight Due Length
Tutorial quiz 4 weekly quizzes
Online quizzes in Canvas.
10% Multiple weeks 5 multiple choice questions
Outcomes assessed: LO1
Assignment Topic 1 tutorials
Write a working Python script
7.5% Week 03 n/a
Outcomes assessed: LO2 LO3 LO4
Assignment Topic 2 tutorials
Write a working Python script
7.5% Week 04 n/a
Outcomes assessed: LO2 LO3 LO4
Assignment Topic 3 tutorials
Write a working Python script
7.5% Week 05 n/a
Outcomes assessed: LO2 LO3 LO4
Assignment Topic 4 tutorials
Write a working Python script
7.5% Week 06 n/a
Outcomes assessed: LO2 LO3 LO4
Online task In-class test
In-class test, completed online.
60% Week 07 60 minutes.
Outcomes assessed: LO1 LO4 LO3 LO2

Assessment summary

  • 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
  • In-class test -  The in-class test 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

Description

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. 

Distinction

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.

Credit

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.

Pass

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.

Fail

0 - 49

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

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.

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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

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

Equity, Access and Diversity statement

The School of Physics recognises that biases, bullying and discrimination, including but not limited to those based on gender, race, sexual orientation, gender identity, religion and age, continue to impact parts of our community disproportionately. Consequently, the School is strongly committed to taking effective steps to make our environment supportive and inclusive and one that provides equity of access and opportunity for everyone.


The School has Equity Officers as a point of contact for students who may have a query or concern about any issues relating to equity, access and diversity. If you feel you have been treated unfairly, discriminated against, bullied or disadvantaged in any way, you are encouraged to talk to one of the Equity Officers or any member of the Physics staff.


More information can be found at https://sydney.edu.au/science/schools/school-of-physics/equity-access-diversity.html

 

Any student who feels they may need a special accommodation based on the impact of a disability should contact Disability
Services: https://sydney.edu.au/study/academic-support/disability-support.html who can help arrange support.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

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.