IFN509 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: | IFN509 |
|---|---|
| Prerequisite(s): | (IFN554 or IFQ554 or IFN581 or IFN555 or IFQ555 or IFN582) OR (192cps in IV04 or IV05 or EV08 or EV07 or LV41) OR (admission into IV54 or IV59 or IV58 or IV60) OR (admission into IN15 or IN17 or EN72 or EN75 or EN76 or EN77) IFN554, IFQ554, IFN555 and IFQ555 or IFN581 or IFN582 can be enrolled in the same teaching period as IFN509. |
| Antirequisite(s): | INN342, INN343 |
| Equivalent(s): | IFQ509 |
| Assumed Knowledge: | Basic statistics functions |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,164 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $5,352 |
Unit Outline: Semester 1 2025, Gardens Point, Internal
| Unit code: | IFN509 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFN554 or IFQ554 or IFN581 or IFN555 or IFQ555 or IFN582) OR (192cps in IV04 or IV05 or EV08 or EV07 or LV41) OR (admission into IV54 or IV59 or IV58 or IV60) OR (admission into IN15 or IN17 or EN72 or EN75 or EN76 or EN77) IFN554, IFQ554, IFN555 and IFQ555 or IFN581 or IFN582 can be enrolled in the same teaching period as IFN509. |
| Equivalent: | IFQ509 |
| Assumed Knowledge: | Basic statistics functions |
| Anti-requisite: | INN342, INN343 |
| Coordinator: | Yue Xu | yue.xu@qut.edu.au |
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 analyse 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
- 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 First Nation Australians.
- Work independently and in a team to implement a data analytics project.
- Communicate professionally in written and visual formats the findings of 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 extraction and selection across various data types, such as text, web and social media. You will delve into fundamental data science lifecycle, learning how to design and model complex data problems using various datasets. 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 subject will be delivered through the following means:
Lectures (2 hours) provide the theoretical basis of the subject.
Tutorials (1 hour) provide an opportunity to work in groups on case studies to investigate and develop data science concepts.
Practicals (1 hour) allow you to apply theory to practical (industry data-driven) problems using available software tools.
The learning process will be focused on real-world scenarios. Emphasis will be placed on theoretical work, laboratory exercises and case studies using active learning approaches. The exercises will be designed to reinforce key concepts and assist in completing 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 progress throughout the unit through asking the teaching staff for advice and assistance during lectures and practical sessions.
The assessments will be marked according to a criteria sheet and returned to you within two weeks of submission.
You can request private consultation with teaching staff.
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 by applying data manipulation strategies and techniquesthat could have been implemented to solveameliorate or deal with the given issues. problems.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
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.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: On campus (in-person) Exam
Quiz / short answer questions and more advanced problems covering the whole of the semester teaching content.
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.
Requirements to Study
Costs
There are no extraordinary charges or costs are associated with the requirements for this unit.
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.EN72 Master of Advanced Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Advanced Robotics and AI and Data Analytics domains
Relates to: On campus (in-person) Exam - Critically analyse, evaluate and apply appropriate methods to problems to achieve research-informed solutions in Advanced Robotics and AI and Data Analytics domains
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply systematic approaches to plan, design, execute and manage projects in Advanced Robotics and AI and Data Analytics domains
Relates to: Project (applied) - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Project (applied) - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project (applied)
EN75 Master of Sustainable Infrastructure with Data Analytics
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Data Analytics domains
Relates to: On campus (in-person) Exam - Analyse and evaluate problems in Sustainable Infrastructure and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure and Data Analytics domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Project (applied) - Effectively communicate problems in Sustainable Infrastructure and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Project (applied) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Project (applied)
EN76 Master of Renewable Energy with Data Analytics
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Renewable Energy and Data Analytics domains
Relates to: On campus (in-person) Exam - Analyse and evaluate problems in Renewable Energy and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Renewable Energy and Data Analytics domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Project (applied) - Effectively communicate problems in Renewable Energy and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Project (applied) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Project (applied)
EN77 Master of Advanced Manufacturing with Data Analytics
- Demonstrate and apply advanced and specialist theory-based discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Advanced Manufacturing and Data Analytics domains
Relates to: On campus (in-person) Exam - Employ advanced specialist technical skills, analysis approaches, design, and data to the solution of problems in Advanced Manufacturing and Data Analytics domains, critically evaluating solutions and practice-informed performance to deliver whole of life requirements and strategic objectives
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Implement professional communication and collaborative skills while engaging with stakeholders, exchanging ideas, and presenting complex information to specialist and non-specialist audiences in Advanced Manufacturing and Data Analytics domains
Relates to: Project (applied) - Demonstrate ethical and socially responsible practice, recognising the importance of personal accountability, and reflective practice, risk-informed judgements, and leadership
Relates to: Project (applied)
IN20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
Relates to: ULO1, Problem Solving Task, Project (applied), On campus (in-person) 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), On campus (in-person) 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) - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: ULO5, Problem Solving Task, Project (applied) - 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) - Demonstrate business acumen and well-developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: ULO7, Problem Solving Task, Project (applied) - 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, Project (applied), On campus (in-person) Exam
IN30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: Project (applied) - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: Problem Solving Task, Project (applied) - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Appraise personal values, attitudes and performance in your continuing professional development.
Relates to: Problem Solving Task, Project (applied) - Reflect on social and ethical data science issues, including how these relate to First Nations Australians.
Relates to: Project (applied), On campus (in-person) Exam
IN31 Master of Data Science
- Demonstrate advanced knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Skilfully use appropriate statistical, computational, and modelling techniques to derive insights from data to support decision-making.
Relates to: Problem Solving Task, On campus (in-person) Exam - Critically apply specialist problem-solving approaches to design, execute and produce data science solutions.
Relates to: Project (applied) - Work effectively both independently and collaboratively in project teams to enable successful processes and outcomes.
Relates to: Problem Solving Task, Project (applied) - Communicate effectively and succinctly in oral, written and visual formats for diverse purposes and audiences.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Appraise personal values, attitudes and performance in your continuing professional development.
Relates to: Problem Solving Task, Project (applied) - Critically reflect on social and ethical data science issues, including how these relate to First Nations Australians.
Relates to: Project (applied), On campus (in-person) Exam
LV41 Bachelor of Biomedical Science
- Critically review, analyse and synthesise foundational knowledge in a broad range of biomedical discipline areas and in depth theoretical, technical and practical knowledge in specialised discipline areas.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Demonstrate the cognitive skills required to find solutions to scientific problems.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Contribute effectively to biomedical projects, either as an individual or as a member of a team, by demonstrating professional behaviour and participating in continuous learning.
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply knowledge and skills to rapidly source, critically analyse and communicate biomedical science information using appropriate technologies.
Relates to: Project (applied)
Unit Outline: Semester 2 2025, Gardens Point, Internal
| Unit code: | IFN509 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFN554 or IFQ554 or IFN581 or IFN555 or IFQ555 or IFN582) OR (192cps in IV04 or IV05 or EV08 or EV07 or LV41) OR (admission into IV54 or IV59 or IV58 or IV60) OR (admission into IN15 or IN17 or EN72 or EN75 or EN76 or EN77) IFN554, IFQ554, IFN555 and IFQ555 or IFN581 or IFN582 can be enrolled in the same teaching period as IFN509. |
| Equivalent: | IFQ509 |
| Assumed Knowledge: | Basic statistics functions |
| Anti-requisite: | INN342, INN343 |
| Coordinator: | Yue Xu | yue.xu@qut.edu.au |
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 analyse 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
- 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 First Nation Australians.
- Work independently and in a team to implement a data analytics project.
- Communicate professionally in written and visual formats the findings of 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 extraction and selection across various data types, such as text, web and social media. You will delve into fundamental data science lifecycle, learning how to design and model complex data problems using various datasets. 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 subject will be delivered through the following means:
Lectures (2 hours) provide the theoretical basis of the subject.
Tutorials (1 hour) provide an opportunity to work in groups on case studies to investigate and develop data science concepts.
Practicals (1 hour) allow you to apply theory to practical (industry data-driven) problems using available software tools.
The learning process will be focused on real-world scenarios. Emphasis will be placed on theoretical work, laboratory exercises and case studies using active learning approaches. The exercises will be designed to reinforce key concepts and assist in completing 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 progress throughout the unit through asking the teaching staff for advice and assistance during lectures and practical sessions.
The assessments will be marked according to a criteria sheet and returned to you within two weeks of submission.
You can request private consultation with teaching staff.
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 by applying data manipulation strategies and techniquesthat could have been implemented to solveameliorate or deal with the given issues. problems.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
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.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: On campus (in-person) Exam
Quiz / short answer questions and more advanced problems covering the whole of the semester teaching content.
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.
Requirements to Study
Costs
There are no extraordinary charges or costs are associated with the requirements for this unit.
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.EN72 Master of Advanced Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Advanced Robotics and AI and Data Analytics domains
Relates to: On campus (in-person) Exam - Critically analyse, evaluate and apply appropriate methods to problems to achieve research-informed solutions in Advanced Robotics and AI and Data Analytics domains
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply systematic approaches to plan, design, execute and manage projects in Advanced Robotics and AI and Data Analytics domains
Relates to: Project (applied) - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Project (applied) - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project (applied)
EN75 Master of Sustainable Infrastructure with Data Analytics
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Data Analytics domains
Relates to: On campus (in-person) Exam - Analyse and evaluate problems in Sustainable Infrastructure and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure and Data Analytics domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Project (applied) - Effectively communicate problems in Sustainable Infrastructure and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Project (applied) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Project (applied)
EN76 Master of Renewable Energy with Data Analytics
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Renewable Energy and Data Analytics domains
Relates to: On campus (in-person) Exam - Analyse and evaluate problems in Renewable Energy and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Renewable Energy and Data Analytics domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Project (applied) - Effectively communicate problems in Renewable Energy and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Project (applied) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Project (applied)
EN77 Master of Advanced Manufacturing with Data Analytics
- Demonstrate and apply advanced and specialist theory-based discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Advanced Manufacturing and Data Analytics domains
Relates to: On campus (in-person) Exam - Employ advanced specialist technical skills, analysis approaches, design, and data to the solution of problems in Advanced Manufacturing and Data Analytics domains, critically evaluating solutions and practice-informed performance to deliver whole of life requirements and strategic objectives
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Implement professional communication and collaborative skills while engaging with stakeholders, exchanging ideas, and presenting complex information to specialist and non-specialist audiences in Advanced Manufacturing and Data Analytics domains
Relates to: Project (applied) - Demonstrate ethical and socially responsible practice, recognising the importance of personal accountability, and reflective practice, risk-informed judgements, and leadership
Relates to: Project (applied)
IN20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
Relates to: ULO1, Problem Solving Task, Project (applied), On campus (in-person) 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), On campus (in-person) 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) - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: ULO5, Problem Solving Task, Project (applied) - 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) - Demonstrate business acumen and well-developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: ULO7, Problem Solving Task, Project (applied) - 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, Project (applied), On campus (in-person) Exam
IN30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: Project (applied) - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: Problem Solving Task, Project (applied) - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Appraise personal values, attitudes and performance in your continuing professional development.
Relates to: Problem Solving Task, Project (applied) - Reflect on social and ethical data science issues, including how these relate to First Nations Australians.
Relates to: Project (applied), On campus (in-person) Exam
IN31 Master of Data Science
- Demonstrate advanced knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Skilfully use appropriate statistical, computational, and modelling techniques to derive insights from data to support decision-making.
Relates to: Problem Solving Task, On campus (in-person) Exam - Critically apply specialist problem-solving approaches to design, execute and produce data science solutions.
Relates to: Project (applied) - Work effectively both independently and collaboratively in project teams to enable successful processes and outcomes.
Relates to: Problem Solving Task, Project (applied) - Communicate effectively and succinctly in oral, written and visual formats for diverse purposes and audiences.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Appraise personal values, attitudes and performance in your continuing professional development.
Relates to: Problem Solving Task, Project (applied) - Critically reflect on social and ethical data science issues, including how these relate to First Nations Australians.
Relates to: Project (applied), On campus (in-person) Exam
LV41 Bachelor of Biomedical Science
- Critically review, analyse and synthesise foundational knowledge in a broad range of biomedical discipline areas and in depth theoretical, technical and practical knowledge in specialised discipline areas.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Demonstrate the cognitive skills required to find solutions to scientific problems.
Relates to: Problem Solving Task, Project (applied), On campus (in-person) Exam - Contribute effectively to biomedical projects, either as an individual or as a member of a team, by demonstrating professional behaviour and participating in continuous learning.
Relates to: Problem Solving Task, On campus (in-person) Exam - Apply knowledge and skills to rapidly source, critically analyse and communicate biomedical science information using appropriate technologies.
Relates to: Project (applied)