CAB330 Data and Web Analytics
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | CAB330 |
|---|---|
| Prerequisite(s): | CAB220 or DSB100 or CAB230 or IAB207 |
| Equivalent(s): | INB342 |
| Assumed Knowledge: | Familiarity with the following IT concepts at the introductory level: Elementary Statistics; Basic Database Concepts; Finding library resources; and Issues involved in aligning business technology and information systems are assumed knowledge. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,164 |
| Domestic tuition unit fee | $4,356 |
| International unit fee | $5,172 |
Unit Outline: Semester 2 2025, Gardens Point, Internal
| Unit code: | CAB330 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | CAB220 or DSB100 or CAB230 or IAB207 |
| Equivalent: | INB342 |
| Assumed Knowledge: | Familiarity with the following IT concepts at the introductory level: Elementary Statistics; Basic Database Concepts; Finding library resources; and Issues involved in aligning business technology and information systems are assumed knowledge. |
| Coordinator: | Richi Nayak | r.nayak@qut.edu.au |
Overview
Data analytics has become a popular way to support decision-making by turning an organization's large collection of data into useful knowledge about their customers and business processes. Data analytics has direct applications in several fields such as social networks, business processes, search-engines, e-commerce, digital libraries, bioinformatics and web information systems. This unit provide fundamental knowledge and skills of data analytics to help with data-driven decision making. You will learn the different types of data mining techniques to apply classification, clustering and association mining. You will learn how the processing can be applied to text and web usage data. This is an introductory unit and the knowledge and skills developed in this unit are relevant to all IT professionals.
Learning Outcomes
On successful completion of this unit you will be able to:
- Theoretical and technical understanding of AI-driven data and Web analytics methods and tools in solving real-world problems.
- Plan and manage AI projects effectively from the start and avoid pitfalls in data preparation, modelling, and results interpretation.
- Identify suitable opportunities for data and web mining, and seamlessly integrate AI solutions into the business and technical ecosystems of organisations.
- Collaborate effectively in small teams to optimise managerial decision-making in AI projects.
- Collaborate effectively in small teams to optimise managerial decision-making in AI projects.
Content
The following topics will be covered.
- Introduction to Data Mining and Knowledge Discovery
- The knowledge discovery process and methodology;
- Data preparation for knowledge discovery
- Classification and prediction
- Clustering
- Link Analysis
- Text Mining
- Web Mining
Learning Approaches
This subject will be delivered through the following means:
- Pre-recorded lectures which provide the theoretical basis of the subject;
- Practicals (2 hours) which allow you to apply theory to practical (industry data-driven) problems using available software tools and implementation exercises.
- Interactive Q&A session (1 hour) weekly
The learning process will be focused on real-world scenarios. Emphasis will be placed on theoretical work, laboratory exercises and case studies. The review exercises will be designed to reinforce key concepts and to assist in the completion of assessments. Problem handling assessments will be drawn from typical industry applications and real world data sources. You are also encouraged to use data from your field of interest.
Feedback on Learning and Assessment
You can obtain feedback on your progress throughout the unit through asking the teaching staff for advice and assistance during lectures and practical sessions. You are encouraged to contact the lecturer personally for seeking feedback. The assessments will be marked according to a criteria sheet and returned to you within two weeks of submission.
Assessment
Overview
The assessments in this unit are designed for you to demonstrate a critical understanding of the data and web analytics concepts acquired during the lectures, as well as the application of these concepts in real-world application settings acquired during practicals. The quizzes will allow you to demonstrate your understanding of the methods and challenges associated with data and web analytics. Assessment criteria will be made available to you at the introduction of each assessment.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Case Study
Predictive Data Analytics
Case Study 1 includes mining meaningful information from the underlying data after applying predictive mining techniques.
This is an assignment for the purposes of an extension. This policy applies to the group report only.
Assessment: Project (applied)
Descritive Data Mining
Application of clustreing and link analysis on entrprise, document and web data.
This is an assignment for the purposes of an extension. This policy applies to the group report only.
Assessment: Invigilated Examination
This will assess your learning from the entire semester. This exam will consist of MCQ and short and long form questions.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
Followings will also be used in addition to the text book.
- Lecture notes on Canvas.
- Various selected papers from the literature (provided via Canvas).
You are strongly encouraged to read recommended references and articles pertaining to this unit.
No extraordinary charges or costs are associated with the requirements of this unit.
Resource Materials
Prescribed text(s)
Author: J. Han and M. Kamber, Title: Data Mining Concepts and Techniques, Morgan Kaufmann, 2022
This book is available as an e-book in the library. This book mainly contains the material covered in lectures from week 1 to week 8. Sufficient materials will be provided to you via handouts or online links for the lectures from week 9 to week 13.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: ULO5
Unit Outline: Semester 2 2025, Online
| Unit code: | CAB330 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | CAB220 or DSB100 or CAB230 or IAB207 |
| Equivalent: | INB342 |
| Assumed Knowledge: | Familiarity with the following IT concepts at the introductory level: Elementary Statistics; Basic Database Concepts; Finding library resources; and Issues involved in aligning business technology and information systems are assumed knowledge. |
Overview
Data analytics has become a popular way to support decision-making by turning an organization's large collection of data into useful knowledge about their customers and business processes. Data analytics has direct applications in several fields such as social networks, business processes, search-engines, e-commerce, digital libraries, bioinformatics and web information systems. This unit provide fundamental knowledge and skills of data analytics to help with data-driven decision making. You will learn the different types of data mining techniques to apply classification, clustering and association mining. You will learn how the processing can be applied to text and web usage data. This is an introductory unit and the knowledge and skills developed in this unit are relevant to all IT professionals.
Learning Outcomes
On successful completion of this unit you will be able to:
- Theoretical and technical understanding of AI-driven data and Web analytics methods and tools in solving real-world problems.
- Plan and manage AI projects effectively from the start and avoid pitfalls in data preparation, modelling, and results interpretation.
- Identify suitable opportunities for data and web mining, and seamlessly integrate AI solutions into the business and technical ecosystems of organisations.
- Collaborate effectively in small teams to optimise managerial decision-making in AI projects.
- Collaborate effectively in small teams to optimise managerial decision-making in AI projects.
Content
The following topics will be covered.
- Introduction to Data Mining and Knowledge Discovery
- The knowledge discovery process and methodology;
- Data preparation for knowledge discovery
- Classification and prediction
- Clustering
- Link Analysis
- Text Mining
- Web Mining
Learning Approaches
This subject will be delivered through the following means:
- Pre-recorded lectures which provide the theoretical basis of the subject;
- Practicals (2 hours) which allow you to apply theory to practical (industry data-driven) problems using available software tools and implementation exercises.
- Interactive Q&A session (1 hour) weekly
The learning process will be focused on real-world scenarios. Emphasis will be placed on theoretical work, laboratory exercises and case studies. The review exercises will be designed to reinforce key concepts and to assist in the completion of assessments. Problem handling assessments will be drawn from typical industry applications and real world data sources. You are also encouraged to use data from your field of interest.
Feedback on Learning and Assessment
You can obtain feedback on your progress throughout the unit through asking the teaching staff for advice and assistance during lectures and practical sessions. You are encouraged to contact the lecturer personally for seeking feedback. The assessments will be marked according to a criteria sheet and returned to you within two weeks of submission.
Assessment
Overview
The assessments in this unit are designed for you to demonstrate a critical understanding of the data and web analytics concepts acquired during the lectures, as well as the application of these concepts in real-world application settings acquired during practicals. The quizzes will allow you to demonstrate your understanding of the methods and challenges associated with data and web analytics. Assessment criteria will be made available to you at the introduction of each assessment.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Case Study
Predictive Data Analytics
Case Study 1 includes mining meaningful information from the underlying data after applying predictive mining techniques.
This is an assignment for the purposes of an extension. This policy applies to the group report only.
Assessment: Project (applied)
Descritive Data Mining
Application of clustreing and link analysis on entrprise, document and web data.
This is an assignment for the purposes of an extension. This policy applies to the group report only.
Assessment: Invigilated Examination
This will assess your learning from the entire semester. This exam will consist of MCQ and short and long form questions.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
Followings will also be used in addition to the text book.
- Lecture notes on Canvas.
- Various selected papers from the literature (provided via Canvas).
You are strongly encouraged to read recommended references and articles pertaining to this unit.
No extraordinary charges or costs are associated with the requirements of this unit.
Resource Materials
Prescribed text(s)
Author: J. Han and M. Kamber, Title: Data Mining Concepts and Techniques, Morgan Kaufmann, 2022
This book is available as an e-book in the library. This book mainly contains the material covered in lectures from week 1 to week 8. Sufficient materials will be provided to you via handouts or online links for the lectures from week 9 to week 13.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: ULO5