ENN543 Data Analytics and Optimisation
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: | ENN543 |
|---|---|
| Prerequisite(s): | Completion of 288 credit points OR Admission to (BN87 or EN50 or EN55 or EN63 or EN51 or EN54 or EN56 or EN71 or EN75 or EN73 or EN76 or EN74 or EN77) |
| Equivalent(s): | ENN542 |
| Assumed Knowledge: | Undergraduate Engineering Mathematics |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $3,672 |
| International unit fee | $5,040 |
Unit Outline: Semester 2 2024, Gardens Point, Internal
| Unit code: | ENN543 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Completion of 288 credit points OR Admission to (BN87 or EN50 or EN55 or EN63 or EN51 or EN54 or EN56 or EN71 or EN75 or EN73 or EN76 or EN74 or EN77) |
| Equivalent: | ENN542 |
| Assumed Knowledge: | Undergraduate Engineering Mathematics |
| Coordinator: | Simon Denman | s.denman@qut.edu.au |
Overview
There is a growing need for engineers to understand underlying patterns and characteristics of vast amounts of data collected from various sources by performing advanced data analytics and optimisation to transform data into understandable and actionable information for the purpose of making decisions. This unit develops your knowledge to improve the economy and efficiency of systems, processes, and enterprises through data analytics and optimisation. The unit focuses on application of statistical, and optimisation methods to solve complex problems involving large data sets from multiple sources.
Learning Outcomes
On successful completion of this unit you will be able to:
- Formulate and solve complex real world engineering problems as mathematical problems.
- Apply data analytics to interpret and visualise data from multiple sources.
- Select and apply statistical, machine learning and optimisation methods to real world engineering problems.
- Demonstrate effective group skills and communication skills through analytic documentation.
Content
The content of this unit will include:
- Introduction to Machine Learning (Supervised and Unsupervised Learning)
- Single and Multiple Linear Regression including LASSO and Ridge regression models
- Methods for clustering data
- Logistic regression and methods for working with discrete data
- Classification
- Dimension Reduction
- Neural networks
- Classical Optimization Techniques (Single Value, Multi-variable, Convex and Linear Programming)
Learning Approaches
In this unit you can expect to experience the following timetabled activities:
- Weekly lecture consultation sessions (2 hours per week) that provide an opportunity to seek advice and receive feedback. These sessions will also provide an opportunity to explore provided examples in fine detail to better facilitate understanding of the underlying concepts.
- Practical sessions (2 hours per week) that provide an opportunity to explore relevant methods and techniques in a hands-on fashion, working on real-world data.
To complement timetabled activities, you will be provided with videos and detailed examples illustrating relevant techniques that you can access flexibly to complete your learning in this unit.
This unit will use the MATLAB environment to explore data science and machine learning concepts and techniques.
Feedback on Learning and Assessment
This unit provides written feedback on the problem solving tasks and final exam. You also receive formative online feedback on conceptual understanding progressively and peer feedback on project work from another team as well as shared feedback with your tutorial class. Interactive tutorials and laboratory sessions provide many opportunities for ongoing practice, discussion and feedback.
You may also receive feedback in various forms throughout the semester which may include:
1. Rubrics provided to show the expected standard for each criteria in an assessment item
2. Comments returned to you via Canvas.
3. Lecture / consultation sessions may be available for group and individual feedback prior to assessment due dates and on completion of assessments.
4. Generic comments provided via QUT Canvas and in class.
Assessment
Overview
This unit has three assessment items.
1. Project report that responds to a problem-solving tasks using techniques such as clustering, regression and optimisation methods.
2. Project report that responds to problem-solving tasks using techniques such as classification dimensionality reduction and neural networks.
3. Final Exam
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Data Analytics Problem Solving 1
You will submit a report based on data analytics solutions using techniques such as clustering, regression and optimisation methods to real world problems.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Data Analytics Problem Solving 2
You will submit a group report based on data analytics and machine learning solutions using techniques such as classification, dimensionality reduction and neural networks to real world problems.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (written)
Final Exam
You will answer questions and problems related to key concepts in statistics and optimisation covered in this unit during the semester. The exam will be aimed at evaluating conceptual knowledge and application, as well as problem solving ability, for the topics presented throughout the semester.
“On Campus invigilated Exam. If campus access is restricted at the time of the central examination period/due date, an alternative, which may be a timed online assessment, will be offered. Individual students whose circumstances prevent their attendance on campus will be provided with an alternative assessment approach.”
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
Other Resources
You are also required to use the following:
- The unit website on QUT's Canvas
- QUT Library Databases:
- QUT Cite| Write: You can access QUT cite/write online (Free download from QUT library)
Risk Assessment Statement
All commencing students are required to complete the Mandatory Safety Induction
There are no extraordinary risks associated with the classroom/lecture activities in this unit.
When working in laboratories and workshops, you will undertake specific hazard and risk related inductions from your tutors and/or technical staff, which may include personal protective equipment (PPE) requirements; participation is compulsory.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.BN87 Master of Engineering Management
- Apply advanced engineering management knowledge, concepts and practices in managing engineering systems and assets
Relates to: ULO1, ULO2, ULO3, Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Critically analyse and evaluate complex engineering management problems to achieve research informed solutions
Relates to: ULO2, ULO3, Examination (written) - Apply systematic approaches to plan, design, execute and manage an engineering management project
Relates to: ULO3, Data Analytics Problem Solving 1 - Communicate complex information effectively and succinctly, presenting high level reports, arguments and justifications in oral, written and visual forms to professional and non-specialist audiences.
Relates to: ULO4, Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Organise and manage time, tasks and projects independently, and collaboratively demonstrating the values and principles that shape engineering management decision making and professional accountability .
Relates to: ULO4, Data Analytics Problem Solving 2
EN51 Master of Sustainable Infrastructure
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Sustainable Infrastructure
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Analyse and evaluate Sustainable Infrastructure problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Data Analytics Problem Solving 2, Examination (written) - Effectively communicate Sustainable Infrastructure problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Data Analytics Problem Solving 2
EN53 Master of Renewable Energy
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Renewable Energy
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Analyse and evaluate Renwable Energy problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Examination (written) - Effectively communicate Renewable Energy problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2 - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Data Analytics Problem Solving 2
EN54 Master of Advanced Manufacturing
- Demonstrate and apply advanced and specialist theory-based discipline knowledge and concepts as they relate to contemporary practice in Advanced Manufacturing
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Employ advanced specialist technical skills, analysis approaches, design, and data to the solution of problems in Advanced Manufacturing, critically evaluating solutions against practice-informed performance and whole-of-life requirements
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Implement professional communication and collaborative skills while engaging with stakeholders, exchanging ideas, and presenting complex information to specialist and non-specialist audiences
Relates to: Data Analytics Problem Solving 2
EN55 Master of Professional Engineering
- Apply advanced and specialist knowledge, concepts and practices in engineering design, analysis management and sustainability.
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Critically analyse and evaluate complex engineering problems to achieve research informed solutions.
Relates to: Examination (written) - Apply systematic approaches to plan, design, execute and manage an engineering project.
Relates to: Data Analytics Problem Solving 2, Examination (written) - Communicate complex information effectively and succinctly, presenting high level reports, arguments and justifications in oral, written and visual forms to professional and non specialist audiences.
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2 - Organise and manage time, tasks and projects independently, and collaboratively demonstrating the values and principles that shape engineering decision making and professional accountability.
Relates to: Data Analytics Problem Solving 2
EN56 Master of Engineering Technology
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Engineering Technology
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Analyse and evaluate Engineering Technology problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Examination (written) - Effectively communicate Engineering Technology problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2 - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Data Analytics Problem Solving 2
EN63 Graduate Certificate in Renewable Power
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary practice in Renewable Power
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Analyse and evaluate Renewable Power problems using technical approaches informed by contemporary practice to achieve innovative, critically informed solutions
Relates to: Examination (written) - Effectively communicate Renewable Power problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2 - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Data Analytics Problem Solving 2
EN71 Master of Sustainable Infrastructure with Project Management
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Project Management domains
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Analyse and evaluate problems in Sustainable Infrastructure and Project Management domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Data Analytics Problem Solving 2, Examination (written) - Effectively communicate problems in Sustainable Infrastructure and Project Management domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Data Analytics Problem Solving 2
EN73 Master of Renewable Energy with Project Management
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Renewable Energy and Project Management domains
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Analyse and evaluate problems in Renewable Energy and Project Management domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Examination (written) - Effectively communicate problems in Renewable Energy and Project Management domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2 - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Data Analytics Problem Solving 2
EN74 Master of Advanced Manufacturing with Project Management
- Demonstrate and apply advanced and specialist theory-based discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Advanced Manufacturing and Project Management domains
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Employ advanced specialist technical skills, analysis approaches, design, and data to the solution of problems in Advanced Manufacturing and Project Management domains, critically evaluating solutions and practice-informed performance to deliver whole of life requirements and strategic objectives
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - 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 Project Management domains
Relates to: Data Analytics Problem Solving 2
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: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - 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: Data Analytics Problem Solving 2, Examination (written) - 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: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Data Analytics Problem Solving 2
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: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - 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: Examination (written) - 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: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2 - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Data Analytics Problem Solving 2
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: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - 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: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - 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: Data Analytics Problem Solving 2
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Data Analytics Problem Solving 2, Examination (written) - Effectively communicate Engineering problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Data Analytics Problem Solving 1, Data Analytics Problem Solving 2, Examination (written) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Data Analytics Problem Solving 2