IFQ509 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: | IFQ509 |
|---|---|
| Prerequisite(s): | IFQ554 or IFN554 or IN15 or IQ15 |
| Antirequisite(s): | INN342, INN343 |
| Equivalent(s): | IFN509 |
| Assumed Knowledge: | Basis statistics functions |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $3,528 |
Unit Outline: Session 2 2024, QUT Online, Online
| Unit code: | IFQ509 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IN20 students admitted prior to 2020 can apply for requisite waiver or IFN554 or IFQ554 (Concurrent pre-requisite) or IN15 or IQ15 |
| Equivalent: | IFN509 |
| Assumed Knowledge: | Basic statistics functions |
| Anti-requisite: | INN342, INN343 |
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 (CLO1, CLO2).
- Choose and apply exploratory data analysis methods to understand the data (CLO1, CLO3)
- Apply appropriate basic data mining operations and methods to model the underlying data problem. (CLO1, CLO2, CLO3)
- Create reproducible data analysis pipelines (CLO1, CLO3, CLO7)
- Work independently or in a team to implement a data analytics project (CLO4, CLO7)
- Communicate professionally in written and visual formats the findings data analysis project to specialist and non-specialist audiences (CLO5, CLO6)
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 online through structured learning materials and activities.
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. The 17% marks are assigned to the group report and the 3% marks are based on individual components (i.e. a quiz or demo)
This is an assignment for the purposes of an extension.
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 is an assignment for the purposes of an extension.
Assessment: Final Exam - Timed online assessment
Written quiz, short answer questions
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.
Unit Outline: Session 4 2024, QUT Online, Online
| Unit code: | IFQ509 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IN20 students admitted prior to 2020 can apply for requisite waiver or IFN554 or IFQ554 (Concurrent pre-requisite) or IN15 or IQ15 |
| Equivalent: | IFN509 |
| Assumed Knowledge: | Basic statistics functions |
| Anti-requisite: | INN342, INN343 |
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 (CLO1, CLO2).
- Choose and apply exploratory data analysis methods to understand the data (CLO1, CLO3)
- Apply appropriate basic data mining operations and methods to model the underlying data problem. (CLO1, CLO2, CLO3)
- Create reproducible data analysis pipelines (CLO1, CLO3, CLO7)
- Work independently or in a team to implement a data analytics project (CLO4, CLO7)
- Communicate professionally in written and visual formats the findings data analysis project to specialist and non-specialist audiences (CLO5, CLO6)
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 online through structured learning materials and activities.
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. The 17% marks are assigned to the group report and the 3% marks are based on individual components (i.e. a quiz or demo)
This is an assignment for the purposes of an extension.
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 is an assignment for the purposes of an extension.
Assessment: Final Exam - Timed online assessment
Written quiz, short answer questions
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.