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.

Details

Academic unit	Computer Science
Unit code	DATA5703
Unit name	Data Science Capstone Project
Session, year ?	Semester 1, 2020
Attendance mode	Supervision
Location	Camperdown/Darlington, Sydney
Credit points	12

Enrolment rules

Prohibitions ?	DATA5707 or DATA5708 or DATA5709
Prerequisites ?	A candidate for the MDS who has completed 24 credit points from Core or Elective units of study may take this unit.
Corequisites ?	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 Individual weekly report on week 3,4,5; Group proposal report on week 5	10%	Multiple weeks	15 pages
Outcomes assessed:
Assignment	Progress Report Individual weekly report on week 6,7,8; Group progress report on week 9	5%	Multiple weeks	15 pages
Outcomes assessed:
Assignment	Final Report/Deliverable Individual weekly report from week 9 to 13; Group final report on week 13	75%	Multiple weeks	Maximum length is 50 pages
Outcomes assessed:
Presentation	Online Presentation/Seminar Online Group presentation on week 13	10%	Week 13	TBA: 15-20 minutes
Outcomes assessed:
= group assignment ?

Assessment Overview: The Data Science Capstone project can be in performed in teams of 4 group members. Groups are supervised by tutors. A mark for project proposal, progress report, presentation and final report/deliverable, respectively, consists of an individual component and group component.
Proposal and Progress Report *: A project plan and progress report of around 15 pages is required that includes problem/task specification, literature survey, proposed methodology, expected outcomes, progress and proposed timeline. Each team member delivers a contribution report as well as reporting on individual work performed.
Online Presentation/Seminar *: Online presentation. 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 *: 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. While the thesis project work is conducted in groups, the final project itself must be written and submitted individually. Students should closely consult the Project Guidelines handout and Marking Sheet for content and formatting requirements. Each team member delivers a contribution report as well as reporting on individual work performed.

* 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.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Project and supervisor negotiation/allocation; 2. Kick-off Meeting	Workshop (1 hr)
Week 02	1. Complete project and supervisor allocation; 2. Project work	Individual study (19 hr)
Week 03	Project work	Individual study (19 hr)
Week 04	Project work	Individual study (19 hr)
Week 05	Project work	Individual study (19 hr)
Week 06	Project work	Individual study (19 hr)
Week 07	Project work	Individual study (19 hr)
Week 08	Project work	Individual study (19 hr)
Week 09	Project work	Individual study (19 hr)
Week 10	Project work	Individual study (19 hr)
Week 11	Project work	Individual study (19 hr)
Week 12	Project work	Individual study (19 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 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 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

Closing the loop

This is the first time this unit has been offered

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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

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

DATA5703: Data Science Capstone Project

Overview

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect