Course coordinator
Dr Slava Vaisman
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
Industry capstone projects are available to students who are eligible to complete DATA7901. Industry capstone projects are an opportunity for students to work on a project in collaboration and onsite with industry through their course.
Students who intend to complete an industry capstone project are required to advise the EAIT Student Employability Team by completing the Industry capstone project EOI form, which will be provided by the course coordinator, by no later than Week 4 of the semester prior to commencing DATA7901. i.e. if enrolling to take this course in Semester 1, you will need to submit your EOI to complete an industry capstone project by Week 4 of the previous semester. Due to the lead time required to source and fill industry capstone projects via a competitive selection process, late EOI’s will not be accepted.
All other students will complete their capstone projects as academic research projects. No Industry capstone project EOI is required if you intend to complete an academic research project. Your course coordinator will discuss academic research options further at the commencement of the semester in which you intend to complete this course.
The capstone project will enable students to bring together their technical, analytic and interpretive skills to execute an end-to-end data science project in a scientific, government or industry setting. This course represents the first part of the capstone project and is focussed on problem formulation, and articulation of the proposal and pitch for the project.ﾠ
Course requirements
Assumed background
Students should have completed all courses in their study plan from Part B1 of the Master of Data Science course list. In addition students should have completed at least DATA7001 and DATA7002.ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
Completion of minimum 8 units towards Master of Data Science program.
Companion or co-requisite courses
You'll need to complete the following courses at the same time:
DATA7201 and DATA7202
Restrictions
Restricted to MDataSc students only.
Course contact
Course staff
Lecturer
Dr Slava Vaisman
Dr Lisa Kelly
Professor Helen Huang
Dr Julian Andrew Steele
Casual academic
Ms Patricia Sheehan
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
All classes will be conducted on campus. Consult your personal timetable for times and locations. Students are expected to attend these sessions in person unless they have a valid reason for being unable to attend (such as illness).
Important: if you are ill, then do not attend any classes in person. Alternative arrangements can be organised – consult Blackboard for details.
Aims and outcomes
The capstone project will focus on tackling a data science problem sourced from science, government or industry. Capstone projects can be research oriented or development oriented. Specifically, the course aims to equip students with the knowledge and skills needed to perform the design section of a data science project. This involves a precise problem formulation and effective communication with the project associates such as clients, managers, and investors.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Formulate a data science problem within a given application context by expressing your ideas clearly and by using words and sentence structures efficiently.
LO2.
Synthesise information from a variety of sources (e.g. academic, business, scientific) and modalities (verbal, visual) to form a communication plan.
LO3.
Make a convincing argument by structuring your ideas coherently and persuasively, and use visual aids and digital technology effectively to engage the audience.
LO4.
Apply effective communication skills to explain data science problems to key stakeholders.
LO5.
Design technically feasible data science solutions with consideration of ethical and legal aspects, and identify appropriate data science technique (techniques) to a practical problem.
LO6.
Apply project design and scoping techniques to write a detailed project proposal suitable for professionals and stakeholders.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Participation/ Student contribution |
Project finalization form submission
|
Pass/Fail |
29/07/2024 3:00 pm |
| Tutorial/ Problem Set |
Writing assignment
|
10% |
30/08/2024 3:00 pm |
| Paper/ Report/ Annotation |
Proposal
|
60% |
20/09/2024 3:00 pm |
| Presentation |
The Seminar
|
30% |
11/10/2024 3:00 pm |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Project finalization form submission
- Hurdle
- In-person
- Online
- Mode
- Written
- Category
- Participation/ Student contribution
- Weight
- Pass/Fail
- Due date
29/07/2024 3:00 pm
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03
Task description
Details will be given in Blackboard.
Hurdle requirements
If you fail this assessment, then your overall mark will be capped at 49% and your final grade will be capped at 3.Submission guidelines
The project finalization form will be submitted in 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
You will receive a mark of 0 if this assessment is submitted late.
Writing assignment
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 10%
- Due date
30/08/2024 3:00 pm
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03
Task description
Details will be given in Blackboard.
Submission guidelines
The assignment will be submitted in 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.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 7-14 days where the earlier time frame applies if no extensions.
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.
Proposal
- In-person
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 60%
- Due date
20/09/2024 3:00 pm
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Details will be given in Blackboard.
Submission guidelines
The proposal will be submitted in 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.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 7-14 days where the earlier time frame applies if no extensions.
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.
The Seminar
- Hurdle
- In-person
- Mode
- Activity/ Performance
- Category
- Presentation
- Weight
- 30%
- Due date
11/10/2024 3:00 pm
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Details will be given in Blackboard.
Hurdle requirements
If your seminar mark is less than 40% then your overall mark will be capped at 49% and your final grade will be capped at 3.Submission guidelines
The seminar slides will be submitted in 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.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 7-14 days where the earlier time frame applies if no extensions.
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 | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. Course grade description: Fails to demonstrate most or all of the basic requirements of the course |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. Course grade description: Demonstrates clear deficiencies in understanding and applying fundamental concepts; communicates information or ideas in ways that are frequently incomplete or confusing and give little attention to the conventions of the discipline |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Demonstrates superficial or partial or faulty understanding of the fundamental concepts of the field of study and limited ability to apply these concepts; presents undeveloped or inappropriate or unsupported arguments; communicates information or ideas with lack of clarity and inconsistent adherence to the conventions of the discipline |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Demonstrates adequate understanding and application of the fundamental concepts of the field of study; develops routine arguments or decisions and provides acceptable justification; communicates information and ideas adequately in terms of the conventions of the discipline |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Demonstrates substantial understanding of fundamental concepts of the field of study and ability to apply these concepts in a variety of contexts; develops or adapts convincing arguments and provides coherent justification; communicates information and ideas clearly and fluently in terms of the conventions of the discipline |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: As for 5, with frequent evidence of originality in defining and analysing issues or problems and in creating solutions; uses a level, style and means of communication appropriate to the discipline and the audience |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: As for 6, with consistent evidence of substantial originality and insight in identifying, generating and communicating competing arguments, perspectives or problem solving approaches; critically evaluates problems, their solutions and implications |
Additional course grading information
- Your final mark (percentage) will be rounded to the nearest whole number before cutoffs are applied.
- If you fail either of the pass/fail assessment items, then your overall mark will be capped at 49% and your final grade will be capped at 3.
- If your seminar mark is less than 40% then your overall mark will be capped at 49% and your final grade will be capped at 3.
Supplementary assessment
Supplementary assessment is not available for some items in this course.
Not all of the assessment components of this course may be re-assessed with supplementary assessment. A grade of 3 or N does not guarantee that supplementary assessment may be undertaken for this course, however students may apply.
Supplementary assessment can take any form. The supplementary assessment in this course will be an assignment similar in style to the regular course assignments. A passing grade is required in this examination to be granted a passing grade in the course.
Additional assessment information
Use of AI Tools
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI
technologies to develop responses is strictly prohibited and may constitute student 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.
Other course materials
If we've listed something under further requirement, you'll need to provide your own.
Required
| Item | Description | Further Requirement |
|---|---|---|
| Course Blackboard Site |
Additional learning resources information
All School of EECS courses have Blackboard sites which can be found at https://learn.uq.edu.au.
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 |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Seminar |
Seminar Learning outcomes: L01, L02, L03, L04, L05, L06 |
Not Timetabled |
Data Science Capstone Project (Part 1) 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, L06 |
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: