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Unit of study_
DATA5709: Data Science Capstone Project - Individual
2024 unit information
The Data Science Capstone project unit provides an opportunity for high-achieving students (WAM of 75+) to carry out an individual defined piece of work with academics of our school. The students will acquire skills including the capacity to define a project, show how it relates to existing work, and carry out the project in a systematic manner. Students will apply their gained knowledge of units of study in the data science domain (MDS). The results will be presented in a final project presentation and report. The unit aims to provide students with the opportunity to carry out an advanced project work in a setting and manner that fosters the development of data science skills in research or design.
Unit details and rules
Managing faculty or University school:
Computer Science
| Code | DATA5709 |
|---|---|
| Academic unit | Computer Science |
| Credit points | 12 |
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Prerequisites:
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A candidate for the MDS who has a WAM of 75+ and has completed 24 credit points from (COMP5046 or COMP5048 or COMP5310 or COMP5313 or COMP5318 or COMP5328 or COMP5329 or COMP5338 or COMP5339 or COMP5349 or COMP5425 or INFO5060 or QBUS6810 or QBUS6840 or STAT5003) |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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DATA5702 or DATA5704 or DATA5703 or DATA5707 or DATA5708 or ODAT5707 or ODAT5708 or COMP5802 |
| Assumed knowledge:
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A candidate of [Master of Data Science (2022 and prior) who has completed 24 credit points from (Data Science Core or Data Science Elective) units of study] or [Master of Data Science (2023 onwards) who has completed 36 credit points] who has a WAM of 75 or more may take this unit. |
At the completion of this unit, you should be able to:
- LO1. utilise prior domain knowledge to define and develop a project relevant to a data science domain (MDS)
- LO2. initiate, formulate and plan a semester-long DS project, incorporating risk mitigation strategies and following the plan methodically
- LO3. analyse and synthesise information, draw appropriate conclusions and present those conclusions in context, with due consideration of methods and assumptions involved
- LO4. demonstrate knowledge of recent DS literature and possess an ability to apply investigative research to their own project
- LO5. document, report and present project work undertaken to engage an academic and/or professional audience
- LO6. develop, substantiate and articulate professional positions on issues relevant to the chosen area of practice, critically reflect on and evaluate the outcomes and process of the project
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 ? |
|---|---|---|---|
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Semester 1 2024
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Supervision | Camperdown/Darlington, Sydney | |
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Semester 2 2024
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Supervision | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
Important enrolment information
Departmental permission requirements
If you see the ‘Departmental Permission’ tag below a session, it means you need faculty or school approval to enrol. This may be because it’s an advanced unit, clinical placement, offshore unit, internship or there are limited places available.
You will be prompted to apply for departmental permission when you select this unit in Sydney Student.
Read our information on departmental permission.
Additional advice
This unit requires departmental permission to ensure students are prepared for the project. Students must be enrolled in the Master of Data Science, have obtained a WAM of 75% or greater, and have been granted approval by the Unit Coordinator. Please include evidence of this approval in your permission request for review by the Faculty. Students are required to source a project and academic supervisor prior to enrolment.
