Outline

Overview

The Data Science Capstone project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to choose a research/development project that demonstrates their prior learning in the data science domain (MDS). The results will be presented in a final project presentation and report. It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and manner that fosters the development of data science skills in research or design.

Unit details and rules

Academic unit	Computer Science
Credit points	12
Prerequisites ?	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)
Corequisites ?	None
Prohibitions ?	DATA5702 or DATA5704 or DATA5707 or DATA5708 or DATA5709 or ODAT5707 or ODAT5708 or COMP5802
Assumed knowledge ?	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] may take this unit.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Xi Wu, xi.wu@sydney.edu.au

Assessment

The census date for this unit availability is 2 September 2024

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Project Status Checking report the project progress to clients to seek feedbacks	0%	Multiple weeks	Checking form provided
Outcomes assessed:
Assignment	Final Report/Deliverable Individual weekly report on week 10, 12, 13; Group final report on week 15	70%	Multiple weeks	Individual: 8 pages; Final: Max 50 pages
Outcomes assessed:
Assignment	Progress Report Individual weekly report on week 6,7,8; Group progress report on week 9	10%	Multiple weeks	Individual: 5 pages; Group: 15 pages
Outcomes assessed:
Assignment	Project Proposal Individual weekly report on week 3,4,5; Group proposal report on week 5	10%	Multiple weeks	Individual: 5 pages; Group: 15 pages
Outcomes assessed:
Presentation	Presentation Group presentation	10%	STUVAC	Recording:15 minutes; Q&A: 20 minutes
Outcomes assessed:
Participation	Tutorial Presentation each student must present their progress weekly in the tutorial	0%	Weekly	5 minutes per student during tutorial
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Assessment Overview: The Data Science Capstone project can be in performed in teams of 5-6 group members. Groups are supervised by tutors. A mark for project proposal, progress report, presentation and final report/deliverable, respectively, consists of both individual component and group component.
Individual Weekly Report: An individual weekly report of around 5 to 8 pages is required from week 3 to week 13. Each team member delivers a report including their own contribution as well as reporting on group collaboration.
Proposal and Progress Report *: Group project proposal and group progress report of around 15 pages are required that include problem/task specification, literature survey, proposed methodology, expected outcomes, progress and proposed timeline.
Presentation *: Each student will be required to participate in an oral presentation, which is compulsory. Failure to deliver a scheduled presentation seminar will result in a fail grade for the project units.
Final Report *: Maximum length of group final report is 50 pages (including tables, figures and references, but not appendices). Students should closely consult the report template and marking sheet for content and formatting requirements.
Project Status Checking: groups are going to report the project status to clients and seek feedback from clients.
Tutorial Presentation: each student is going to present their progress in the weekly tutorial, which is compulsory for this unit.

* indicates assessment tasks which must be repeated if a student misses it due to special considerations.

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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

It is a requirement of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

There may be statistically defensible moderation when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with unit outcomes.

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 10 calendar days late, a mark of 0 will be awarded.

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	1. Project allocation; 2. Kick-off Lecture	Lecture (2 hr)
Week 02	1. Complete project allocation; 2. Independent Project work; 3. Meeting with clients; 4. Attending workshop	Project (19 hr)
Week 03	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 04	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 05	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 06	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 07	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 08	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 09	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 10	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 11	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 12	1.Independent Project work; 2. Meeting with clients; 3. Attending workshop	Project (19 hr)
Week 13	Presentation and Reports	Seminar (2 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 12 credit point unit, this equates to roughly 240-300 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. 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

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.

The number of individual reports has been reduced from weekly to fortnightly in the last six weeks of the semester.

Additional information

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. Similarity of any submitted assessment cannot be higher than 35%.

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

DATA5703: Data Science Capstone Project

Semester 2, 2024 [Supervision] - 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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect