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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
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Semester 2, 2023
Attendance mode Supervision
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
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DATA5703 or DATA5709. Eligible students of the Data Science Capstone Project may choose either DATA5703 or (DATA5707 and DATA5708) or DATA5709
Prerequisites
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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
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DATA5707
Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator Xi Wu, xi.wu@sydney.edu.au
Administrative staff Xiaofei Liu xiaofei.liu@sydney.edu.au Project Officer/Admin Support
Type Description Weight Due Length
Assignment Final report/deliverable
Conclude the whole project
70% Formal exam period
Due date: 19 Nov 2023 at 23:59
maximum length of 50 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Online task Project proposal
Research Plan in previous semester
10% Progressive 15 pages
Outcomes assessed: LO2 LO4
Online task Progress Report 1
Progress Report in previous semester
4% Progressive 10 pages
Outcomes assessed: LO1 LO6 LO4 LO2
Online task hurdle task Project Status Checking
Report the project state and seek feedback and confirmation
0% Progressive checking form provided
Outcomes assessed: LO2 LO4 LO1
Assignment hurdle task Presentation
An oral presentation
10% STUVAC
Due date: 12 Nov 2023 at 23:59
Up to 30 minutes
Outcomes assessed: LO1 LO6 LO5 LO4 LO3
Assignment Progress Report 2
Report the progress
6% Week 06
Due date: 10 Sep 2023 at 23:59
10 pages
Outcomes assessed: LO1 LO6 LO4 LO3
hurdle task = hurdle task ?
  • 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 of around 15 pages and progress report of around 10 pages is required from each student. Should include problem/task specification, literature survey, proposed methodology, expected outcomes, progress and proposed timeline.
  • Presentation *: Each student will be required to participate in an oral 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.
  • Project Status Checking: students are going to report the project state to supervisor(s) and seek feedback and confirmation. It is a compulsory component for this project unit.

​* 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 markers.

Students work individually and will have their individual contribution assessed.

Students will receive a mark of UC (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.

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.

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 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.

WK Topic Learning activity Learning outcomes
Week 01 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 02 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 03 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 04 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 05 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 06 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 07 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 08 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 09 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 10 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 11 1. Independent project work; 2. Meeting with supervisor Project (10 hr) LO1 LO3 LO4 LO5 LO6
Week 12 1. Independent project work; 2. Meeting with supervisor Project (10 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 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 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
No significant 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.

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