Course coordinator
Dr Nan Ye
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
The capstone project will enable students to bring together their technical, analytic and interpretive skills to execute a project in a specified data science field. This course represents the second part of the capstone project and is focussed on the implementation and conclusion of the project proposed during DATA7901.
The capstone project will enable students to bring together their technical, analytic and interpretive skills to execute an end-to-end data science project. Each semester two focused project areas in Data Science will be offered to students, one drawn from the Statistics/Mathematics sub-field of data science and theﾠother from the Computer Science/IT sub-fieldﾠof Data Science. Students will implement one or more data science analytic techniques to explore/model/categorise a data set (or sets).ﾠ Students will undertake individual projects within the focused problem area. It is intended that different student projects areﾠcomplimentary, and allow students to learn a breadth of analysis methods, alongside depth in one analysis technique. In exceptional circumstances, projects from outside the two focused areas will be allowed. This courseﾠrepresents the second part of the capstone project and is focussed on the implementation and conclusion of the project proposed during DATA7901.ﾠ
Course requirements
Assumed background
Students should complete this course in their final year of Master of Data Science. This course is the continuation of DATA7901 which needs to be successfully completed prior to this course.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
DATA7901 and (DATA7201 or DATA7202)
Restrictions
MDataSc students only. Students seeking External enrolment should contact the School for permission (studentenquiries@itee.uq.edu.au)
Jointly taught details
This course is jointly-taught with:
- DATA7902
All learning activities are jointly taught.
Course contact
Aims and outcomes
This capstone project will focus on tackling a data science problem drawn from either the Statistics/Mathematics sub-field of data science or from the Computer Science/IT sub-field of Data Science. Capstone projects can be research oriented or development oriented. Specifically, the course give students the opportunity to formulate a data science problem and implement its solution in a given application context. The students will communicate their solution to an academic audience.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Synthesise information from a variety of sources to develop informed solutions.
LO2.
Implement a data science approach to solving a problem within a given application context.
LO3.
Apply, optimise and evaluate appropriate data science techniques to a practical problem, taking into account ethical and legal aspects.
LO4.
Present project outcomes in a coherent manner with compelling arguments and appropriate use of visual aids and digital technology.
LO5.
Write a technical project report that clearly describes the problem, motivation, methodology and findings, with justifications where necessary.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Presentation | Presentation | 40% |
14/10/2024 - 18/10/2024 |
| Paper/ Report/ Annotation | Final report | 60% |
6/11/2024 2:00 pm |
Assessment details
Presentation
- Mode
- Activity/ Performance
- Category
- Presentation
- Weight
- 40%
- Due date
14/10/2024 - 18/10/2024
- Learning outcomes
- L01, L02, L03, L04
Task description
Each student must verbally and visually present the results of their work at a time negotiated with their supervisor. The duration of the presentation will be 20 minutes including questions. The presentation may include a demonstration of software produced during the project. An electronic copy of the slides used in the presentation is to be submitted to the supervisor at the time of the presentation.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final report
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 60%
- Due date
6/11/2024 2:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The final report reports on the results from the student's project. It should be written as to be understandable by persons other than the supervisor, and should comprehensively include material on the problems and goals of the project, applicable methods, the approach taken, major decisions and the reasons for the selection of goals and methods, results, the extent to which the goals have been achieved, the relevance, importance and context of achievements and the reasons for any shortcomings.
Submission guidelines
You must submit your final report through the Turnitin link provided on Blackboard.
Any supplementary files may be uploaded as a single zip file on Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: Students will receive a grade of 1 if their final mark is less than 20% and they have submitted at least one piece of assessment. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: Students will receive a grade of 2 if they meet all of the following criteria: an overall mark of at least 20%, not satisfy the criteria for a higher grade. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Students will receive a grade of 3 if they meet all of the following criteria: an overall mark of at least 45%, not satisfy the criteria for a higher grade. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Students will receive a grade of 4 if they meet all of the following criteria: an overall mark of at least 50%, a final report mark of at least 40%, a presentation mark of at least 40%, not satisfy the criteria for a higher grade. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Students will receive a grade of 5 if they meet all of the following criteria: an overall mark of at least 65%, a final report mark of at least 50%, a presentation mark of at least 50%, not satisfy the criteria for a higher grade. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Students will receive a grade of 6 if they meet all of the following criteria: an overall mark of at least 75%, a final report mark of at least 65%, a presentation mark of at least 65%, not satisfy the criteria for a higher grade. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Students will receive a grade of 7 if they meet all of the following criteria: an overall mark of at least 85%, a final report mark of at least 75%, a presentation mark of at least 75%. |
Additional course grading information
Your overall percentage will be calculated as per the assessment item weights above and then rounded to the nearest whole percent. The course coordinator reserves the right to moderate marks.
Supplementary assessment
Supplementary assessment is not available for some items in this course.
This course is the second half of a year-long capstone project and is partially exempt from supplementary assessment. Should you fail a course with a grade of 3, or a non-graded ‘N’, you may be eligible for supplementary assessment. The supplementary assessment will involve a re-assessment of one or more assessment items.
Additional assessment information
Use of AI Tools
The assessment tasks in this course evaluate students’ abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute misconduct under the Student Code of Conduct.
Having Troubles?
If you are having difficulties with any aspect of the course material you should seek help. Speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Not scheduled |
Not Timetabled |
Data Science Project Build This runs through the entire semester. It is important to remember that you largely drive your project. You are responsible for managing the timely completion of your project, generating ideas, working through difficulties, searching for appropriate resources, and completing your project deliverables on time. Your supervisor's role should be as an advisor and mentor. Learning outcomes: L01, L02, L03, L04, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: