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. 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 IT skills in research or design. Eligible students for the Data Science Capstone project will be required to complete both DATA5707 (6 CPS) and DATA5708 (6 CPS), totalling 12 CPS.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	A part-time enrolled candidate for the MDS who has completed 24 credit points from Core or Elective units of study may take this unit.
Corequisites ?	DATA5707
Prohibitions ?	DATA5703. Eligible students of the Data Science Capstone Project may choose either DATA5703 or DATA5707/DATA5708.
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

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

Type	Description	Weight	Due	Length
Assignment	Project proposal Research Plan in previous semester	10%	Progressive	15 pages
Outcomes assessed:
Assignment	Progress Report 1 Progress Report in previous semester	1%	Progressive	5 pages
Outcomes assessed:
Assignment	Progress Report 2 Progress Report in previous semester	1%	Progressive	5 pages
Outcomes assessed:
Assignment	Progress Report 3 Report the progress	3%	Week 06	5 pages
Outcomes assessed:
Presentation	Online Presentation/Seminar An online presentation/seminar	10%	Week 13	15-20 minutes
Outcomes assessed:
Assignment	Final report/deliverable Conclude the whole project	75%	Week 13	maximum length of 50 pages
Outcomes assessed:

Assessment summary

Assessment Overview: The Data Science Capstone Project is performed as an individual expert who is working with clients and other stakeholders.
Proposal and progress report *: A Research plan and progress report of around 15 pages is required from each student. Should include problem/task specification, literature survey, proposed methodology, expected outcomes, progress and proposed timeline.
Online presentation/seminar *: Each student will be required to participate in an online presentation. Participation in presentations is compulsory. Failure to deliver a scheduled seminar will result in a fail grade for the project units.
Final report *: Maximum length 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.

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

The main assessment tasks are based on three areas (depending on the nature of the task the first two might be combined into a single document):

The deliverables from the project that would be given to the `client` (who may be external, internal to the School, or even an implied type of person who would desire this work to be done, without there being a concrete individual). Example deliverables could be some software, an installed system, a report discussing some alternatives, an analysis of a marketplace, a design, etc;
A final report on the project for the supervisor, which would include an account of the purpose and context, a detailed description of the process which took place, an evaluation of the outcomes and the process;
An online presentation/seminar of the project outcomes for an audience of both client and supervisor.

Students work individually and will have their individual contribution assessed.

Students will receive a mark of UCN (Unit Continuing) for Data Science Capstone A if they have shown sufficient progress to warrant continuing on to Data Science Capstone Project B. The final grade for Data Science Capstone A and B is based on the work done in Data Science Capstone Project A and B as a whole. Any marks awarded in Data Science Capstone A will be incorporated into calculations for the final grade of the two units.

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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Kick-off meeting; 2. Project work	Online class (10 hr)
Week 02	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 03	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 04	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 05	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 06	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 07	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 08	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 09	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 10	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 11	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 12	1. Independent project work; 2. Meeting with supervisor	Individual study (10 hr)
Week 13	Online Presentation and Reports	Online class (1 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. utilise prior domain knowledge to define and develop a research/development project relevant to a data science domain (MDS)
LO2. initiate, formulate and plan a DS research project based on research and development
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 research 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
LO7. plan a semester-long project, incorporating risk mitigation strategies and follow the plan methodically

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.

No changes have been made since this unit was last offered

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

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

DATA5708: Data Science Capstone B

Semester 1, 2021 [Supervision] - Remote

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

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