Course profile

Data Mining (INFS4203)

Study period
Sem 2 2025
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 2, 2025 (28/07/2025 - 22/11/2025)
Study level
Undergraduate
Location
St Lucia
Attendance mode
In Person
Units
2
Administrative campus
St Lucia
Coordinating unit
Elec Engineering & Comp Science School

Techniques used for data cleaning, finding patterns in structured, text and web data; with application to areas such as customer relationship management, fraud detection and homeland security.

This course introduces the concepts and techniques in Data Mining and Knowledge Discovery from Databases. Students taking this course are expected to be already familiar with concepts of databases, algorithms and data structures. This course will provide a good introduction to analysing large volumes of data. The lectures are designed to discuss the problems and solutions in data mining, including data classification and clustering, anomaly detection, mining association rules, and data mining with text and web data.

Course changes in response to previous student feedback: The assessment scheme has been revised to enhance fairness and better reflect students’ practical understanding. Instead of a written proposal, students will now complete a presentation and a code interview, providing more interactive and skills-based evaluation aligned with real-world practices.

Course requirements

Assumed background

Students are assumed to have the background knowledge covered in INFS2200/INFS7903 (Relational Database Systems) and CSSE1001/7030 (Introduction to Software Engineering).ﾠWhile COMP3506/COMP7505ﾠ(Algorithms and Data Structures) is not a prerequisite of this course, it is beneficial for students to have taken that course in their early studies.ﾠ ﾠ

Prerequisites

You'll need to complete the following courses before enrolling in this one:

(CSSE1001 or ENGG1001) and INFS2200

Incompatible

You can't enrol in this course if you've already completed the following:

INFS7203

Jointly taught details

This course is jointly-taught with:

  • INFS7203

All learning activities and assessments (except the final exam) are shared across both courses. While the final exam is held at the same time for both cohorts, the content is partially differentiated.

Course contact

Course coordinator

Dr Miao Xu

During the semester, students should post all course-related questions — including those about assignments, exams, or lecture content — on Ed Discussion in the first instance, so that the teaching team can respond efficiently and responses can benefit all students.

For personal matters (e.g., private concerns), please contact the teaching team via email.

In-person consultations are by appointment only and must be arranged in advance via email.

Course staff

Lecturer

Timetable

The timetable for this course is available on the UQ Public Timetable.

Additional timetable information

Applied Class will commence in Week 2.

Please note that Wednesday, 13 August, is a public holiday and no applied class will be held on that day. To support students who regularly attend the Wednesday sessions, a recording will be provided during Week 3.

Aims and outcomes

Aims and outcomes

The focus of this course is to provide a comprehensive introduction to data mining. The areas covered include association analysis,ﾠclassification, clustering, text mining and web mining.ﾠThe goal is to present fundamental concepts and algorithms for each topic, thus providing the students with the necessary background for the application of data mining to real problems. In addition, this course also provides a starting point for those students who are interested in pursuing research in data mining or related fields.
Course overview
Assessment

Assessment

Assessment summary

Category Assessment task Weight Due date
Quiz In-Class Quiz
  • Online
 15%

8/09/2025 9:30 am

The quiz is scheduled to be from 8:00 to 9:30 during the scheduled lecture time in week 7, with a reading time of 10 minutes and an exam time of 80 minutes.
Computer Code, Paper/ Report/ Annotation, Project Project Report
  • Hurdle
  • Online
 20%

20/10/2025 1:00 pm
Presentation, Project Project Presentation and Code Interview
  • Hurdle
  • Identity Verified
  • In-person
 15%

Week 13 - Week 13

Each student will be allocated an 8-minute slot during their enrolled Applied Class Session (APP) in Week 13, including a 5-minute presentation and a 3-minute code interview. The schedule will be published on Blackboard in Week 11.
Examination Final Examination
  • Hurdle
  • Identity Verified
  • In-person
 50%

End of Semester Exam Period

8/11/2025 - 22/11/2025

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

In-Class Quiz

  • Online
Mode
Written
Category
Quiz
Weight
15%
Due date

8/09/2025 9:30 am

The quiz is scheduled to be from 8:00 to 9:30 during the scheduled lecture time in week 7, with a reading time of 10 minutes and an exam time of 80 minutes.

Other conditions
Time limited.

See the conditions definitions

Learning outcomes
L01, L02, L05, L06, L08, L09

Task description

  1. This is an online, non-invigilated quiz delivered via Blackboard. Students may complete the quiz from any location; however, they are responsible for ensuring reliable internet access and a stable technical environment.
  2. It is an individual assessment, covering lecture content from Week 2 to Week 6.
  3. The quiz consists of multiple-choice questions.
  4. Course staff will be available on Zoom during the quiz to answer clarification questions.
  5. Pre-approval is required for any special arrangements.


This is an open-book, non-invigilated multiple-choice quiz. Students may use generative AI or Machine Translation (MT) tools to support their understanding of course content. However, students are expected to complete the quiz independently and based on their own understanding. The teaching team reserves the right to interview students to confirm their comprehension. Failure to demonstrate sufficient understanding may constitute student misconduct under the Student Code of Conduct.

Submission guidelines

The quiz will be administered via Blackboard.

Deferral or extension

You may be able to defer this exam.

A deferred quiz will be made available.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

This assessment takes place in a scheduled class, so no late submissions will be accepted.

This has been approved by the Associate Dean (Academic).

Project Report

  • Hurdle
  • Online
Mode
Activity/ Performance
Category
Computer Code, Paper/ Report/ Annotation, Project
Weight
20%
Due date

20/10/2025 1:00 pm

Learning outcomes
L01, L02, L03, L05, L06, L07, L08, L09

Task description

  1. This assignment is designed to assess your ability to implement data mining techniques to solve real-world problems.
  2. The report and the code must be submitted as two separate files via Blackboard by 1 pm, Monday of Week 12. Detailed submission requirements will be provided in the project specification, to be released later.
  3. The assignment brief, including detailed criteria and the marking rubric, will be released in Week 4 on Blackboard.

This task has been designed to be challenging, authentic, and complex. While students may use generative AI and/or machine translation (MT) technologies, successful completion of this assessment will require critical engagement with the specific context and task, for which such tools will offer only limited support. Failure to appropriately reference the use of generative AI or MT tools may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students must be able to demonstrate clear and independent understanding of their submission, beyond the use of AI or MT tools.

Important:

A code interview will be conducted in Week 13. Students will participate in the code interview based on their submitted implementation. The main purpose of this interview is to verify that students understand the code they have submitted, and that it reflects their own work, rather than content generated or copied without comprehension (e.g., from generative AI tools). If your code is genuinely your own and you understand what you have written, there is no need to worry about this component. If a student receives less than 35% in the code interview, the mark for this assignment will be capped at 10 out of 20, regardless of the quality of the submitted report and code.

Please note that only the final submitted version will be marked, and the late penalty will apply based on the timestamp of that version.

Hurdle requirements

If a student receives fewer than 2.1 out of 6 for the code interview, the total mark for "Project Report" will be capped at 10 out of 20, regardless of the quality of the code, the report or the presentation.

Submission guidelines

Please refer to the project specification for details. Please ensure that your code and report are submitted in accordance with the detailed requirements outlined in the project specification. Failure to submit the required files in the correct format may result in a mark of zero.

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.

Marks and/or feedback will be provided to students within 21 days after the submission of this assessment.

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.

Even if a proper extension is granted for the “Project Report” assessment, a version of the code must be submitted no later than 27 October 2025 in order to participate in the code interview.

Students may still submit their final version during the late submission window (with applicable penalties). However, only the final submitted version will be marked, and the late penalty will be applied based on its submission time.

The final submission must not differ substantially from the version used during the interview. As a guideline, if the final code differs by more than 40% from the interview version, the teaching team reserves the right to either conduct a follow-up interview based on the final submission or assign a mark of zero for the code interview component, regardless of performance in the original interview.

Project Presentation and Code Interview

  • Hurdle
  • Identity Verified
  • In-person
Mode
Oral
Category
Presentation, Project
Weight
15%
Due date

Week 13 - Week 13

Each student will be allocated an 8-minute slot during their enrolled Applied Class Session (APP) in Week 13, including a 5-minute presentation and a 3-minute code interview. The schedule will be published on Blackboard in Week 11.

Other conditions
Time limited.

See the conditions definitions

Learning outcomes
L01, L02, L03, L05, L07, L08, L09

Task description

This 15-mark assessment is based on the coding project submitted in the previous week ("Project Report"). It consists of two parts delivered in a single 8-minute session:

  1. Presentation (5 minutes, 9 marks): Students will give a short in-class presentation explaining their approach, design decisions, and key outcomes.
  2. Code Interview (3 minutes, 6 marks): Following the presentation, each student will participate in a brief individual interview focused on their submitted code. The goal is to verify that the code reflects the student's own work and that they understand what they have written.

The code interview serves as the secure assessment component of the course, in line with Faculty policy on academic integrity.

⚠️ Important: If a student receives fewer than 2.1 out of 6 for the code interview, the total mark for "Project Report" will be capped at 10 out of 20, regardless of the quality of the code, the report or the presentation. This ensures that all submitted work demonstrates genuine understanding and authorship.

  • Students must submit at least one working version of their code by 27 October 2025, even if an approved extension has been granted for the Project Report. This early version is required to be eligible for the code interview. A later submission may be accepted under an approved extension, but should not significantly differ from the version submitted by the deadline. (Please refer to the “Project Report” assessment Late Submission notes for further details)
  • Students who fail to submit any code by 27 October 2025 will receive 0 for the Code Interview, although the Presentation may still proceed as scheduled.
  • Assessment sessions will be held during each student’s allocated Applied Class Session (APP) in Week 13. Guidelines will be released on Blackboard in Week 7, and schedules will be released on Blackboard in Week 11.
  • Make-up interviews may be arranged in approved deferred cases (e.g., illness), typically in the Review Week or early Exam Week. Unapproved absence will result in a mark of zero. see LATE SUBMISSION NOTES for details.

This assessment task is to be completed in person, and the use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted during the presentation or interview session. Students may use AI or MT tools during the preparation phase (e.g., to assist with slide development), but any such use must be clearly and appropriately referenced. Failure to reference the use of generative AI or MT tools, or the attempted use of such tools during the live assessment, may constitute student misconduct under the Student Code of Conduct.

If your implementation is your own and you understand what you wrote, the interview will be straightforward. This is not intended to penalise honest work, but to ensure fairness and academic integrity.


Hurdle requirements

If a student receives fewer than 2.1 out of 6 for the code interview, the total mark for "Project Report" will be capped at 10 out of 20, regardless of the quality of the code, the report or the presentation.

Submission guidelines

Students will present and be interviewed during their allocated Applied Class Session (APP) in Week 13. No online submission is required for this assessment.

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.

If you are unable to attend your allocated session, one reschedule is permitted. To apply for a reschedule, you need to apply for an extension via my.UQ.

If your extension request is approved, your make-up session may take place during revision week or the examination period.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

This is a scheduled assessment. Students who miss their allocated session without an approved extension will receive a mark of zero.

Final Examination

  • Hurdle
  • Identity Verified
  • In-person
Mode
Written
Category
Examination
Weight
50%
Due date

End of Semester Exam Period

8/11/2025 - 22/11/2025

Other conditions
Time limited, Secure.

See the conditions definitions

Learning outcomes
L01, L02, L03, L04, L05, L06, L08, L09

Task description

More information will be released in the last lecture (i.e., the course revision).

This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.

Hurdle requirements

Students must receiveﾠa passing grade on the final exam in order to pass this course (i.e., achieve at least 50% of the final exam). If you fail the exam, your final mark will be capped at 49 and your final grade will be capped at 3.

Exam details

Planning time 10 minutes
Duration 120 minutes
Calculator options

(In person) Casio FX82 series only or UQ approved and labelled calculator
Open/closed book Closed book examination - no written materials permitted
Exam platform Paper based
Invigilation

Invigilated in person

Submission guidelines

Deferral or extension

You may be able to defer this exam.

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.
2 (Fail) 20 - 46

Minimal evidence of achievement of course learning outcomes.
3 (Marginal Fail) 47 - 49

Demonstrated evidence of developing achievement of course learning outcomes
4 (Pass) 50 - 64

Demonstrated evidence of functional achievement of course learning outcomes.
5 (Credit) 65 - 74

Demonstrated evidence of proficient achievement of course learning outcomes.
6 (Distinction) 75 - 84

Demonstrated evidence of advanced achievement of course learning outcomes.
7 (High Distinction) 85 - 100

Demonstrated evidence of exceptional achievement of course learning outcomes.

Additional course grading information

Students must receiveﾠa passing grade on the final exam in order to pass this course (i.e., achieve at least 50% of the final exam). If you fail the exam, your final mark will be capped at 49 and your final grade will be capped at 3.

Overall marks will be rounded to the nearest integer before grade cut-offs are applied.

Supplementary assessment

Supplementary assessment is available for this course.

Additional assessment information

Having Troubles?

If you are having difficulties with any aspect of the course material, you should seek help and 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.


Aims and outcomes
Learning resources

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.

Assessment
Learning activities

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

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Learning period Activity type Topic
Multiple weeks

From Week 1 To Week 13
(28 Jul - 02 Nov)
Lecture

Lectures

The course lectures will provide in-depth knowledge of various concepts and techniques in data mining.

Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09
Multiple weeks

From Week 2 To Week 13
(04 Aug - 02 Nov)
Applied Class

Applied Class

Starting from Week 2, applied classes will allow students to further practice the concepts and algorithms introduced in the lectures through examples, exercises, and problem-solving activities.

Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09

Additional learning activity information

An optional formative quiz will be released by Monday of Week 5 to help students assess their understanding of the course content prior to Census Date. The quiz will provide feedback to support students’ self-evaluation.

Students who are considering withdrawing from the course before Census Date and would like to discuss their concerns are encouraged to schedule a meeting by emailing the course coordinator during Week 5. Please allow at least two business days to arrange the meeting — students should email no later than Wednesday of Week 5 to ensure availability.

Learning resources
Policies and procedures

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:

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:

Learning activities