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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_

DATA5708: Data Science Capstone B

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.

Details

Academic unit Computer Science
Unit code DATA5708
Unit name Data Science Capstone B
Session, year
? 
Semester 1, 2020
Attendance mode Supervision
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
? 
DATA5703. Eligible students of the Data Science Capstone Project may choose either DATA5703 or DATA5707/DATA5708.
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
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 in previous semester
10% - 15 pages
Outcomes assessed: LO2 LO7
Assignment Progress Report 1
Progress Report in previous semester
1% - 15 pages
Outcomes assessed: LO2 LO4 LO7
Assignment Progress Report 2
Progress Report in previous semester
1% - 15 pages
Outcomes assessed: LO2 LO4 LO7
Assignment Progress Report 3
15 pages
3% Week 06 Report the progress
Outcomes assessed: LO2 LO4 LO7
Presentation Online Presentation/Seminar
An online presentation/seminar
10% Week 13 15-20 minutes
Outcomes assessed: LO1 LO6 LO5 LO3 LO2
Assignment Final report/deliverable
Conclude the whole project
75% Week 13 maximum length of 50 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
  • 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 in first semester and proposed timeline.
  • 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 *: A 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 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.

​* 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):

  1. 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;
  2. 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;
  3. 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.

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. Kick-off meeting; 2. Project work Workshop (10 hr) LO1 LO3 LO4 LO5 LO6
Week 02 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 03 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 04 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 05 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 06 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 07 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 08 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 09 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 10 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 11 1. Independent project work; 2. Meeting with supervisor Individual study (10 hr) LO1 LO3 LO4 LO5 LO6
Week 12 1. Independent project work; 2. Meeting with supervisor Individual study (10 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 6 credit point unit, this equates to roughly 120-150 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.

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