IFQ509 Introduction to Data Science
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: | IFQ509 |
|---|---|
| Prerequisite(s): | IFQ581 or IFN581 or ((IFQ555 and IFQ556) or (IFN555 and IFN556)) OR IFQ582 or IFN582 or ((IFQ554 and IFQ557) or (IFN554 and IFN557) or (IFQ552 and IFQ554) or (IFN552 and IFN554)) OR admission into IN15 or IQ15. IQ20 or IN20 students admitted prior to 2020 can apply for requisite waiver for IFQ554 or IFN554. (IFN555 and IFN556) or (IFQ555 and IFQ556) or (IFN554 and IFN557) or (IFQ554 and IFQ557) and IFN581 or IFQ581 or IFN582 or IFQ582 can be enrolled in the same teaching period as IFN509. IN30 and IQ30 cannot waive the prerequisite. |
| Equivalent(s): | IFN509 |
| Assumed Knowledge: | Basis statistics functions |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,192 |
| Domestic tuition unit fee | $4,116 |
| International unit fee | $5,616 |
Unit Outline: Session 1 2026, QUT Online, Online
| Unit code: | IFQ509 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IFQ581 or IFN581 or ((IFQ555 and IFQ556) or (IFN555 and IFN556)) OR IFQ582 or IFN582 or ((IFQ554 and IFQ557) or (IFN554 and IFN557) or (IFQ552 and IFQ554) or (IFN552 and IFN554)) OR admission into IN15 or IQ15. IQ20 or IN20 students admitted prior to 2020 can apply for requisite waiver for IFQ554 or IFN554. (IFN555 and IFN556) or (IFQ555 and IFQ556) or (IFN554 and IFN557) or (IFQ554 and IFQ557) and IFN581 or IFQ581 or IFN582 or IFQ582 can be enrolled in the same teaching period as IFN509. IN30 and IQ30 cannot waive the prerequisite. |
| Equivalent: | IFN509 |
| Assumed Knowledge: | Basic statistics functions |
Overview
This fundamental data science unit addresses the core concepts, techniques and practices in data science. In the information age, with large amounts of data produced and made available every minute, data exploration and mining have become necessary for individuals and organisations to unlock the power of data. This unit will introduce you to various data exploration and mining methods to manipulate, model and analyze data. You will explore the complete data science lifecycle and also the importance of data ethics and privacy, and issues of fairness and diversity in data collection, analysis, and algorithmic decision-making.
This is an introductory unit and the knowledge and skills developed in this unit are relevant to both data science and non-data science majors. This unit also allows you to review your personal values, attitudes, and goals set for data science learning including consideration of sustainability concerns.
Learning Outcomes
On successful completion of this unit you will be able to:
- Articulate problems in alignment with data science principles, with an awareness of how GenAI is used by Data Scientists.
- Process and examine data to highlight its fundamental patterns and structures
- Develop and fine-tune data models
- Identify, evaluate, and rectify issues linked to ethics, equity, bias, and diversity in data-driven models including issues relating to Aboriginal and Torres Strait Islanders.
- Work independently and in a team to implement a data analytics project.
- Communicate professionally in written and visual formats the findings data analysis project to specialist and non-specialist audiences.
- Reflect on personal capabilities and appraise oneself in relation to expectations for data science professionals.
Content
Data science is a multi-disciplinary field that includes methods from statistics, mathematics, machine learning, data mining, artificial intelligence and computer science, to find valuable insights from datasets. In this unit, you will learn the techniques for exploratory data analysis, enabling you to cleanse, manipulate and represent data in order to derive simple insights from large data repositories. You will learn how to investigate and prevent issues around data quality. You will develop a strong understanding of abstract data representations, including feature selection across various data types, such as text, transactional data, and social media data. You will delve into fundamental data science lifecycle, learning how to design and model complex data problems using various data exploration and processing methods, and also obtaining basic knowledge regarding how GenAI is used in Data Science industry. Moreover, you will explore the principles of responsible data science, emphasising the importance of maximising access to high-quality data while minimising the potential for data misuse that could jeopardise fundamental rights and erode public trust in data science technologies. Equip yourself with the basic skills and knowledge needed to navigate the data-driven future while positively impacting the world.
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity to participate in online discussions with peers and teaching staff.
The learning process will be focused on real-world scenarios. Emphasis will be placed on theoretical work, laboratory exercises and case studies. The 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 data sources.
Feedback on Learning and Assessment
You can obtain feedback on your learning and progress throughout the unit through:
- formative in-class individual and whole-of-class feedback provided by unit staff during discussion activities
- responses to questions posed through the unit communication channel from your peers and teaching staff
- feedback given on your assessment items individually via the rubric and written feedback.
Assessment
Overview
This unit includes two summative assessment items during the semester and a final examination. Summative assessments are designed to give students a hands-on approach to dealing with various issues that arise in a data-driven analysis. Students will be provided with real-world case studies for analysing data quality, conducting data pre-processing, data exploration and data mining, and following best practices of responsible data science. Students will learn how to choose a particular method appropriate to the problem at hand, and similar tasks provided in tutorials and practicals will help them complete the assessment items successfully. More details of each assessment task are given below.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
Analyze a given data science case, develop a data science workflow, identify key ethics issues relating to the data science case study, and demonstrate the ability to identify appropriate solutions that could have been implemented to ameliorate or deal with the issues.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied) and Oral Presentation with Q&A
This assessment consists of two parts, project and oral presentation.
- The project part requires you to develop solutions by applying data exploration and manipulation techniques to the given datasets, designing and implementing a data mining project based on analysis of a data analysis scenario and development of data analytics workflow for solving the given problems.
- The oral presentation part requires you to summarise your project work orally and address questions regarding this assessment individually.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Invigilated Final Exam
Written quiz, short answer questions.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
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
Where applicable, online resources will be provided to the student as additional materials.
Resource Materials
Recommended text(s)
J. Han, M. Kamber and J. Pei, Data Mining: Concepts and Techniques, 3rd edition, 2012
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ19 Graduate Diploma in Information Technology
- Demonstrate advanced IT knowledge in one or more IT disciplines.
Relates to: ULO1, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate solutions.
Relates to: ULO2, ULO4, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Apply advanced, industry-best practice, IT methods, tools and techniques to develop and implement IT systems, processes and/or software.
Relates to: ULO3, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Work effectively in both self-directed and collaborative contexts.
Relates to: ULO5, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Communicate effectively in IT professional contexts using written, visual and oral formats.
Relates to: ULO6, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Demonstrate developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: ULO7, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Critically reflect on the social, cultural, ethical and diversity issues related to the IT field including how they relate to First Nations Australians and diverse populations.
Relates to: ULO4, Problem Solving Task, Invigilated Final Exam
IQ20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline
Relates to: ULO1, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: ULO2, ULO4, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO3, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others
Relates to: ULO5, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Communicate effectively in IT professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: ULO6, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Demonstrate business acumen and well-developed values, attitudes, behaviours and judgements in professional contexts.
Relates to: ULO7, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Create positive change through critically reflecting upon and actioning responses to the social, cultural, ethical, sustainability, legal and accessibility issues in the IT field, including how they relate to First Nations Australians and diverse populations.
Relates to: ULO4, Invigilated Final Exam
IQ30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: ULO1, ULO2, ULO3, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: ULO2, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: ULO3, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: ULO5, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: ULO6, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Appraise personal values, attitudes and performance in your continuing professional development.
Relates to: ULO7, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Reflect on social and ethical data science issues, including how these relate to Aboriginal and Torres Strait Islanders.
Relates to: ULO4, Problem Solving Task, Invigilated Final Exam
Unit Outline: Session 3 2026, QUT Online, Online
| Unit code: | IFQ509 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IFQ581 or IFN581 or ((IFQ555 and IFQ556) or (IFN555 and IFN556)) OR IFQ582 or IFN582 or ((IFQ554 and IFQ557) or (IFN554 and IFN557) or (IFQ552 and IFQ554) or (IFN552 and IFN554)) OR admission into IN15 or IQ15. IQ20 or IN20 students admitted prior to 2020 can apply for requisite waiver for IFQ554 or IFN554. (IFN555 and IFN556) or (IFQ555 and IFQ556) or (IFN554 and IFN557) or (IFQ554 and IFQ557) and IFN581 or IFQ581 or IFN582 or IFQ582 can be enrolled in the same teaching period as IFN509. IN30 and IQ30 cannot waive the prerequisite. |
| Equivalent: | IFN509 |
| Assumed Knowledge: | Basic statistics functions |
Overview
This fundamental data science unit addresses the core concepts, techniques and practices in data science. In the information age, with large amounts of data produced and made available every minute, data exploration and mining have become necessary for individuals and organisations to unlock the power of data. This unit will introduce you to various data exploration and mining methods to manipulate, model and analyze data. You will explore the complete data science lifecycle and also the importance of data ethics and privacy, and issues of fairness and diversity in data collection, analysis, and algorithmic decision-making.
This is an introductory unit and the knowledge and skills developed in this unit are relevant to both data science and non-data science majors. This unit also allows you to review your personal values, attitudes, and goals set for data science learning including consideration of sustainability concerns.
Learning Outcomes
On successful completion of this unit you will be able to:
- Articulate problems in alignment with data science principles, with an awareness of how GenAI is used by Data Scientists.
- Process and examine data to highlight its fundamental patterns and structures
- Develop and fine-tune data models
- Identify, evaluate, and rectify issues linked to ethics, equity, bias, and diversity in data-driven models including issues relating to Aboriginal and Torres Strait Islanders.
- Work independently and in a team to implement a data analytics project.
- Communicate professionally in written and visual formats the findings data analysis project to specialist and non-specialist audiences.
- Reflect on personal capabilities and appraise oneself in relation to expectations for data science professionals.
Content
Data science is a multi-disciplinary field that includes methods from statistics, mathematics, machine learning, data mining, artificial intelligence and computer science, to find valuable insights from datasets. In this unit, you will learn the techniques for exploratory data analysis, enabling you to cleanse, manipulate and represent data in order to derive simple insights from large data repositories. You will learn how to investigate and prevent issues around data quality. You will develop a strong understanding of abstract data representations, including feature selection across various data types, such as text, transactional data, and social media data. You will delve into fundamental data science lifecycle, learning how to design and model complex data problems using various data exploration and processing methods, and also obtaining basic knowledge regarding how GenAI is used in Data Science industry. Moreover, you will explore the principles of responsible data science, emphasising the importance of maximising access to high-quality data while minimising the potential for data misuse that could jeopardise fundamental rights and erode public trust in data science technologies. Equip yourself with the basic skills and knowledge needed to navigate the data-driven future while positively impacting the world.
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity to participate in online discussions with peers and teaching staff.
The learning process will be focused on real-world scenarios. Emphasis will be placed on theoretical work, laboratory exercises and case studies. The 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 data sources.
Feedback on Learning and Assessment
You can obtain feedback on your learning and progress throughout the unit through:
- formative in-class individual and whole-of-class feedback provided by unit staff during discussion activities
- responses to questions posed through the unit communication channel from your peers and teaching staff
- feedback given on your assessment items individually via the rubric and written feedback.
Assessment
Overview
This unit includes two summative assessment items during the semester and a final examination. Summative assessments are designed to give students a hands-on approach to dealing with various issues that arise in a data-driven analysis. Students will be provided with real-world case studies for analysing data quality, conducting data pre-processing, data exploration and data mining, and following best practices of responsible data science. Students will learn how to choose a particular method appropriate to the problem at hand, and similar tasks provided in tutorials and practicals will help them complete the assessment items successfully. More details of each assessment task are given below.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
Analyze a given data science case, develop a data science workflow, identify key ethics issues relating to the data science case study, and demonstrate the ability to identify appropriate solutions that could have been implemented to ameliorate or deal with the issues.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied) and Oral Presentation with Q&A
This assessment consists of two parts, project and oral presentation.
- The project part requires you to develop solutions by applying data exploration and manipulation techniques to the given datasets, designing and implementing a data mining project based on analysis of a data analysis scenario and development of data analytics workflow for solving the given problems.
- The oral presentation part requires you to summarise your project work orally and address questions regarding this assessment individually.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Invigilated Final Exam
Written quiz, short answer questions.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
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
Where applicable, online resources will be provided to the student as additional materials.
Resource Materials
Recommended text(s)
J. Han, M. Kamber and J. Pei, Data Mining: Concepts and Techniques, 3rd edition, 2012
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ19 Graduate Diploma in Information Technology
- Demonstrate advanced IT knowledge in one or more IT disciplines.
Relates to: ULO1, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate solutions.
Relates to: ULO2, ULO4, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Apply advanced, industry-best practice, IT methods, tools and techniques to develop and implement IT systems, processes and/or software.
Relates to: ULO3, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Work effectively in both self-directed and collaborative contexts.
Relates to: ULO5, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Communicate effectively in IT professional contexts using written, visual and oral formats.
Relates to: ULO6, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Demonstrate developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: ULO7, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Critically reflect on the social, cultural, ethical and diversity issues related to the IT field including how they relate to First Nations Australians and diverse populations.
Relates to: ULO4, Problem Solving Task, Invigilated Final Exam
IQ20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline
Relates to: ULO1, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: ULO2, ULO4, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO3, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others
Relates to: ULO5, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Communicate effectively in IT professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: ULO6, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Demonstrate business acumen and well-developed values, attitudes, behaviours and judgements in professional contexts.
Relates to: ULO7, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Create positive change through critically reflecting upon and actioning responses to the social, cultural, ethical, sustainability, legal and accessibility issues in the IT field, including how they relate to First Nations Australians and diverse populations.
Relates to: ULO4, Invigilated Final Exam
IQ30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: ULO1, ULO2, ULO3, Problem Solving Task, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: ULO2, Project (applied) and Oral Presentation with Q&A, Invigilated Final Exam - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: ULO3, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: ULO5, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: ULO6, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Appraise personal values, attitudes and performance in your continuing professional development.
Relates to: ULO7, Problem Solving Task, Project (applied) and Oral Presentation with Q&A - Reflect on social and ethical data science issues, including how these relate to Aboriginal and Torres Strait Islanders.
Relates to: ULO4, Problem Solving Task, Invigilated Final Exam