IFN509 Data Exploration and Mining
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): | (Students in IN17 to apply for requisite waiver) or (IFN554 or IFQ554 (Concurrent pre-requisite) or admission to IN15 or IQ15 or IFN555 or IFQ555 (Concurrent pre-requisite)) or (192cps in EV08 or admission to IV58). Note: IFN554, IFQ554, IFN555 and IFQ555 can be enrolled in the same teaching period as IFN509. |
| Antirequisite(s): | INN342, INN343 |
| Equivalent(s): | IFQ509 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $3,528 |
| International unit fee | $4,824 |
Unit Outline: Semester 1 2024, Gardens Point, Internal
| Unit code: | IFN509 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (Students in IN17 to apply for requisite waiver) or (IFN554 or IFQ554 (Concurrent pre-requisite) or enrolment in IN15 or IQ15 or IFN555 or IFQ555 (Concurrent pre-requisite)) or (192cps in EV08 or enrolment in IV58). Note: IFN554, IFQ554, IFN555 and IFQ555 can be enrolled in the same teaching period as IFN509. |
| Equivalent: | IFQ509 |
| Assumed Knowledge: | Basic statistics functions |
| Anti-requisite: | INN342, INN343 |
| Coordinator: | Richi Nayak | r.nayak@qut.edu.au |
Overview
This fundamental data science unit addresses the core concepts, techniques and practices of data exploration and mining. In the information age, with astronomical amounts of data produced and made available every minute, data exploration and mining becomes necessary for individuals and organisations who need to make decisions. With the advancements in data storage technology and the need for automation, data analytics skills are now essential. Data analytics methods enable users to manage, interpret, understand, process and analyse the data to find useful insight. This unit will introduce you to a wide range of data analytics methods and theories to manipulate, model and analyze data.
This is an introductory unit and the knowledge and skills developed in this unit are relevant to both computer science and non-computer science majors.
Learning Outcomes
On successful completion of this unit you will be able to:
- Analyse data driven scenarios and scope data analytics questions.
- Choose and apply exploratory data analysis methods to understand the data and enhance the data quality.
- Apply appropriate basic data mining operations and methods to model the underlying data problem.
- Create reproducible data analysis pipelines.
- Work independently or 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.
Content
Data analytics is a multi-disciplinary field that includes methods from statistics, mathematics, machine learning, data mining, artificial intelligence and computer science, to find useful insights from datasets. In this unit you will learn the exploratory data analysis methods to cleanse, manipulate and represent data to find simple insights and interpretations 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, that is, how to extract and select features from a range of data types (including text, web and social media). You will learn the fundamental data mining operations and methods to model the underlying data problem using the pre-processed datasets.
Learning Approaches
This subject will be delivered through the following means:
- Lectures (2 hours), which provide the theoretical basis of the subject.
- Tutorials (1 hours, which provide an opportunity to work in groups on case studies to investigate and develop data science architectures.
- Computer tutorial (1 hour), which 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. 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 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 deal with various issues that arise in a data driven analysis. Students will be provided with real-world case studies for analysing data quality and for conducting data pre-processing, data exploration and data mining. Students will learn how to choose a particular method that is appropriate to the problem at hand, and you will be given similar tasks in tutorial and practicals that will help them to successfully complete the assessment items. More details of each assessment task are given below
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
Develop solutions by applying data manipulation strategies and techniques to solve the given problems.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
Design and Implement a data mining project based on analysis of a data analysis scenario and development of data analytics workflow, including interpretation and communication of results along with critical reflection regarding the applied methods.
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.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
No extraordinary charges or costs are associated with the requirements for this unit. 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)
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 2024, Gardens Point, Internal
| Unit code: | IFN509 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (Students in IN17 to apply for requisite waiver) or (IFN554 or IFQ554 (Concurrent pre-requisite) or enrolment in IN15 or IQ15 or IFN555 or IFQ555 (Concurrent pre-requisite)) or (192cps in EV08 or enrolment in IV58). Note: IFN554, IFQ554, IFN555 and IFQ555 can be enrolled in the same teaching period as IFN509. |
| Equivalent: | IFQ509 |
| Assumed Knowledge: | Basic statistics functions |
| Anti-requisite: | INN342, INN343 |
| Coordinator: | Richi Nayak | r.nayak@qut.edu.au |
Overview
This fundamental data science unit addresses the core concepts, techniques and practices of data exploration and mining. In the information age, with astronomical amounts of data produced and made available every minute, data exploration and mining becomes necessary for individuals and organisations who need to make decisions. With the advancements in data storage technology and the need for automation, data analytics skills are now essential. Data analytics methods enable users to manage, interpret, understand, process and analyse the data to find useful insight. This unit will introduce you to a wide range of data analytics methods and theories to manipulate, model and analyze data.
This is an introductory unit and the knowledge and skills developed in this unit are relevant to both computer science and non-computer science majors.
Learning Outcomes
On successful completion of this unit you will be able to:
- Analyse data driven scenarios and scope data analytics questions.
- Choose and apply exploratory data analysis methods to understand the data and enhance the data quality.
- Apply appropriate basic data mining operations and methods to model the underlying data problem.
- Create reproducible data analysis pipelines.
- Work independently or 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.
Content
Data analytics is a multi-disciplinary field that includes methods from statistics, mathematics, machine learning, data mining, artificial intelligence and computer science, to find useful insights from datasets. In this unit you will learn the exploratory data analysis methods to cleanse, manipulate and represent data to find simple insights and interpretations 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, that is, how to extract and select features from a range of data types (including text, web and social media). You will learn the fundamental data mining operations and methods to model the underlying data problem using the pre-processed datasets.
Learning Approaches
This subject will be delivered through the following means:
- Lectures (2 hours), which provide the theoretical basis of the subject.
- Tutorials (1 hours, which provide an opportunity to work in groups on case studies to investigate and develop data science architectures.
- Computer tutorial (1 hour), which 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. 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 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 deal with various issues that arise in a data driven analysis. Students will be provided with real-world case studies for analysing data quality and for conducting data pre-processing, data exploration and data mining. Students will learn how to choose a particular method that is appropriate to the problem at hand, and you will be given similar tasks in tutorial and practicals that will help them to successfully complete the assessment items. More details of each assessment task are given below
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
Develop solutions by applying data manipulation strategies and techniques to solve the given problems.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
Design and Implement a data mining project based on analysis of a data analysis scenario and development of data analytics workflow, including interpretation and communication of results along with critical reflection regarding the applied methods.
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.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
No extraordinary charges or costs are associated with the requirements for this unit. 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)
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)