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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

Unit of study_

DATA5709: Data Science Capstone Project - Individual

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.

Details

Academic unit Computer Science
Unit code DATA5709
Unit name Data Science Capstone Project - Individual
Session, year
? 
Semester 1, 2020
Attendance mode Supervision
Location Camperdown/Darlington, Sydney
Credit points 12

Enrolment rules

Prohibitions
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DATA5703 or DATA5707 or DATA5708
Prerequisites
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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
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None
Available to study abroad and exchange students

No

Teaching staff and contact details

Coordinator Xi Wu, xi.wu@sydney.edu.au
Administrative staff Evelyn Riegler, Evelyn.Riegler@sydney.edu.au, Admin Support
Type Description Weight Due Length
Assignment Project Proposal
Research Plan
10% Week 05 15 pages
Outcomes assessed: LO2 LO5
Assignment Progress Report
Report the progress
5% Week 09 15 pages
Outcomes assessed: LO2 LO5 LO7
Presentation Online Presentation/Seminar
An online presentation
10% Week 13 15-20 minutes
Outcomes assessed: LO1 LO6 LO5 LO4 LO3
Assignment Final Report/Deliverable
Conclude the whole project
75% Week 13 Maximum length is 50 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
  • Assessment Overview: The Data Science Capstone project will be performed by a single student. Each student will be supervised by an academic staff 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 in the first semester and proposed timeline.
  • Online presentation/seminar *: An online presentation. Each student will be required to participate in an individual 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 *: Statement identifying the specific contributions of the student and others must be included. Maximum length is 50 pages (including tables, figures and references, but not appendices). The final report must contain a page stating the specific contributions of the student and that of others involved. The thesis project work must be conducted, written and submitted by the student. Students should closely consult the Project Guidelines handout 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.

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.

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 1. Project and supervisor negotiation/allocation; 2. Kick-off Meeting Workshop (1 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 LO5
Week 05 Project work Individual study (19 hr) LO2 LO5 LO7
Week 06 Project work Individual study (19 hr) LO2 LO5 LO7
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 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 Online Presentation and Reports Online class (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
This is the first time this unit has been offered

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.