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Unit of study_

DATA5709: Data Science Capstone Project - Individual

Semester 1, 2023 [Supervision] - Remote

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

Unit code DATA5709
Academic unit Computer Science
Credit points 12
Prohibitions
? 
DATA5703 or DATA5707 or DATA5708
Prerequisites
? 
A candidate for the MDS who has completed 24 credit points from Core or Elective units of study, and has a WAM of 75+ may take this unit
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Xi Wu, xi.wu@sydney.edu.au
Type Description Weight Due Length
Assignment Final Report/Deliverable
Conclude the whole project
70% Formal exam period
Due date: 11 Jun 2023 at 23:59
Maximum length is 50 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment hurdle task Presentation/Seminar
An oral presentation
10% STUVAC
Due date: 04 Jun 2023 at 23:59
15-30 minutes
Outcomes assessed: LO1 LO6 LO5 LO4 LO3
Assignment hurdle task Project Status Checking
check project status with supervisor and confirm expectation and scope
0% Week 04
Due date: 19 Mar 2023 at 23:59
Checking form provided
Outcomes assessed: LO1 LO2 LO4
Assignment Project Proposal
Report the Project Plan
10% Week 05
Due date: 26 Mar 2023 at 23:59
15 pages
Outcomes assessed: LO2 LO4
Assignment Progress Report
Report the progress
10% Week 09
Due date: 30 Apr 2023 at 23:59
15 pages
Outcomes assessed: LO1 LO6 LO4 LO3 LO2
hurdle task = hurdle task ?

Assessment summary

  • Assessment Overview: The Data Science Capstone project will be performed by a single student. Each student will be supervised by an academic or industry member. A mark consists of a project proposal, progress report, presentation and final report/deliverable, components.
  • Proposal and progress report *: A Project Plan and Progress Report of around 15 pages are required that includes problem/task specification, literature survey, proposed methodology, expected outcomes, progress and proposed timeline.
  • Presentation/seminar *: Each student will be required to participate in an individual oral presentation, which is compulsory. Failure to deliver a scheduled 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.

​* indicates an 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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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 1. Project and supervisor negotiation/allocation; 2. Kick-off lecture Online class (2 hr) LO2
Week 02 1. Complete project and supervisor allocation; 2. Project work Individual study (19 hr) LO2
Week 03 Project work Individual study (19 hr) LO2
Week 04 Project work Individual study (19 hr) LO2 LO4
Week 05 Project work Individual study (19 hr) LO2 LO4
Week 06 Project work Individual study (19 hr) LO1 LO3 LO4 LO6
Week 07 Project work Individual study (19 hr) LO1 LO3 LO4 LO6
Week 08 Project work Individual study (19 hr) LO1 LO3 LO4 LO6
Week 09 Project work Individual study (19 hr) LO1 LO3 LO4 LO5 LO6
Week 10 Project work Individual study (19 hr) LO1 LO3 LO4 LO5 LO6
Week 11 Project work Individual study (19 hr) LO1 LO3 LO4 LO5 LO6
Week 12 Project work Individual study (19 hr) LO1 LO3 LO4 LO5 LO6
Week 13 Presentation and Reports Seminar (1 hr) LO1 LO2 LO3 LO4 LO5 LO6

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 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. 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
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

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.