Outline

Overview

The unit will provide an avenue to apply the mathematics concurrently taught in year one, enhance computing skills and data analysis as well as furthering and cementing knowledge provided in AMME1705. The unit is aimed at providing an overview on conducting experiments, data acquisition and data analysis, thus expanding and providing further foundation on the faculty vision of large digital data analysis.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	(AERO1560 or ENGG1800) and (ENGG1810 or INFO1110) and (AMME1705 or ENGF1112) and AMME1802 and AERO1400
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Nicholas Lawson, nicholas.lawson@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Practical skill	Minimum Lecture Attendence A minimum of 80% lecture attendance over the 13 weeks, based on a register	4%	Multiple weeks	Lecture series - all weeks	AI allowed
Outcomes assessed:
Interactive oral	Tutorial 1 - Presentation Assessment of presentation by all group members of material practiced in tutorial.	6%	Week 03 Due date: 12 Mar 2026 at 17:00 Closing date: 30 Jan 2026	Up to 10 mins	AI prohibited
Outcomes assessed:
In-class quiz	Early Assessment Task Quiz Early Assessment Task week 3. Hurdle task to test knowledge gained from lectures up to this point.	3%	Week 03 Due date: 11 Mar 2026 at 15:59 Closing date: 11 Mar 2026	45 minutes (+15 mins to upload)	AI allowed
Outcomes assessed:
Presentation	Tutorial Assessment 1 Group task set and data & method prepared for a presentation at end of each tutorial class (+pdf of presentation)	0%	Week 03 Due date: 12 Mar 2026 at 18:00 Closing date: 12 Mar 2026	task released up to 5 hours prior	AI allowed
Outcomes assessed:
Interactive oral	Tutorial 2 - Presentation Assessment of presentation by all group members of material practiced in tutorial.	6%	Week 05 Due date: 26 Mar 2026 at 17:00 Closing date: 30 Jan 2026	Up to 10 mins	AI prohibited
Outcomes assessed:
Presentation	Tutorial Assessment 2 Group task set and data & method prepared for a presentation at end of each tutorial class (+pdf of presentation)	0%	Week 05 Due date: 26 Mar 2026 at 18:00 Closing date: 26 Mar 2026	task released up to 5 hours prior	AI allowed
Outcomes assessed:
Written work	Individual Report - Lab 1 & 2 Individual report of Lab 1 and Lab 2 data processing, analysis and presentation	15%	Week 06 Due date: 03 Apr 2026 at 23:59 Closing date: 16 Apr 2026	10 - 20 pages	AI allowed
Outcomes assessed:
Interactive oral	Tutorial 3 - Presentation Assessment of presentation by all group members of material practiced in tutorial.	6%	Week 08 Due date: 20 Apr 2026 at 17:00 Closing date: 20 Apr 2026	Up to 10 mins	AI prohibited
Outcomes assessed:
Presentation	Tutorial Assessment 3 Group task set and data & method prepared for a presentation at end of each tutorial class (+pdf of presentation)	0%	Week 08 Due date: 21 Apr 2026 at 18:00 Closing date: 21 Apr 2026	task released up to 5 hours prior	AI allowed
Outcomes assessed:
Written work	Group Report - Lab 3 Group report of Lab 3 data processing, analysis and presentation	12%	Week 09 Due date: 01 May 2026 at 23:59 Closing date: 08 May 2026	10 - 15 pages	AI allowed
Outcomes assessed:
Interactive oral	Tutorial 4 - Presentation Assessment of presentation by all group members of material practiced in tutorial	6%	Week 10 Due date: 07 May 2026 at 17:00 Closing date: 08 May 2026	Up to 10 mins	AI prohibited
Outcomes assessed:
Presentation	Tutorial Assessment 4 Group task set and data & method prepared for a presentation at end of each tutorial class (+pdf of presentation)	0%	Week 10 Due date: 07 May 2026 at 18:00 Closing date: 07 May 2026	task released up to 5 hours prior	AI allowed
Outcomes assessed:
Written work	Group Report - Lab 4 Group report of Lab 4 data processing, analysis and presentation	12%	Week 11 Due date: 15 May 2026 at 23:59 Closing date: 22 May 2026	10 - 15 pages	AI allowed
Outcomes assessed:
Written work	Group Report - Lab 5 Group report of Lab 5 data processing, analysis and presentation	12%	Week 11 Due date: 29 May 2026 at 23:59 Closing date: 05 Jun 2026	10 - 15 pages	AI allowed
Outcomes assessed:
Interactive oral	Lab Group Presentation Group presentation based on data processing methods applied to data from a lab or an experiment	18%	Week 13 Due date: 29 May 2026 at 19:00 Closing date: 29 May 2026	up to 20 mins	AI prohibited
Outcomes assessed:
Presentation	Lab Group Presentation Group presentation prepared based on data processing methods applied to data from a lab or an experiment	0%	Week 13 Due date: 29 May 2026 at 20:00 Closing date: 29 May 2026	task released up to 5 hours prior	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Assignments: Assignment reports will cover the 5 laboratories completed by the groups of students which will cover a cylinder wake, a heated cylinder, a boundary layer transition, a wing bending measurement and a free flight glider. Different groups will be assigned for each report and each group member will receive a weighted mark based on a Sparkplus survey from the rest of the group members for group reports. The first lab will require an individual lab report. All group reports require every group member to complete a SparkPlus survey to receive their mark.
Tutorial Assessments: In 4 of the 5 tutorial sessions, students will be set a basic data processing and data analysis task in their current group and will be required to present data and a solution to the problem at the end of the tutorial session, with a maximum presentation and demonstration of their methods of 10 mins, to all the students. An assessment grading of the final solution will be made based on this demonstration, presentation of the method and the answers given to questions from the lecturer, tutor and their peers. All students must attend these sessions to receive a mark. Non-attendence by any given group member will result in a nil mark for that group member.
Lab Group Presentation: A group presentation of up to 20 mins long must be made by the current set groups of students on a laboratory completed throughout the semester. This laboratory will be set by the lecturer up to two weeks ahead of the presentation date. The assessment mark for each group will be made based on the presentation quality and content from the group and the answers given to questions from the lecturer, tutor and their peers.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (sydney.edu.au) (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result Name	Mark Range	Description
High Distinction	85 – 100	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75 – 84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65 – 74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50 – 65	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty.
Fail	0 – 50	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty. This grade, with corresponding mark, should also be used in cases where a student fails to achieve a mandated standard in a compulsory assessment, thereby failing to demonstrate the learning outcomes to a satisfactory standard. In such cases the student will receive the mark awarded by the faculty up to a maximum of 49.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The penalty for lateness is 5% per day. The penalty would apply from the next calendar day after the deadline. The penalty is a percentage of the available mark and is applied to the mark gained after the submitted work is marked (e.g., an assignment worth 100 marks is 1 day late. The content is given a mark of 75. With the 5% penalty, the final mark is 70).

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Independent study – class preps and report writing	Self-directed learning (65 hr)
Week 01	Intro, data, definitions & error analysis	Lecture (3 hr)
	Laboratory 1 – Background and briefing	Lecture (3 hr)
	Laboratory 1 – Signal / data processing	Lecture (2 hr)
Week 02	Laboratory 2 – Background and briefing	Lecture (3 hr)
	Laboratory 2 – Signal / data processing	Lecture (3 hr)
	Laboratory 1 – Data collection	Practical (1 hr)
Week 03	Laboratory 3 – Background and briefing	Lecture (2 hr)
	Laboratory 3 – Signal / data processing	Lecture (1 hr)
	Tutorial 1 – Signal / data processing / group work	Tutorial (3 hr)
Week 04	Laboratory 4 – Background and briefing	Lecture (2 hr)
Week 04	Laboratory 2 – Data collection	Practical (1 hr)
Week 05	Laboratory 4 – Signal / data processing	Lecture (2 hr)
	Laboratory 5 – Background and briefing	Lecture (2 hr)
	Tutorial 2 – Signal / data processing / group work	Tutorial (3 hr)
Week 06	Laboratory 5 – Signal / data processing	Lecture (1 hr)
Week 07	Laboratory 3 – Data collection	Practical (1 hr)
Week 08	Tutorial 3 – Signal / data processing / group work	Tutorial (3 hr)
Week 08	Laboratory 5 – Signal / data processing	Lecture (2 hr)
Week 09	Aerospace Engineering general data	Lecture (2 hr)
Week 09	Laboratory 4 – Data collection	Practical (7 hr)
Week 10	Aerospace Engineering general data	Lecture (2 hr)
Week 10	Tutorial 4 – Signal / data processing / group work	Tutorial (3 hr)
Week 11	Conclusion	Lecture (2 hr)
Week 11	Laboratory 5 – Data collection	Practical (1 hr)
Week 12	Tutorial 5 – Signal / data processing / group work	Tutorial (3 hr)

Attendance and class requirements

Lectures: Lectures run over the 13 week semester.
Tutorials: Tutorials with examples and group work on open ended problems, to encourage innovation in data processing and analysis methods. Five sessions (4 assessed) with a duration of 3 hours / session. Due to the group nature of the tutorials, all students are required to attend. If students do not attend the tutorials, on presenting their work as a group, at the end of each tutorial, non-attendance will result in a zero mark for that individual for that presentation. The tutorial assessment is based on the presentation given by the groups during the tutorial. A pdf of the presentation must be uploaded to Canvas within an hour of the end of the tutorial session. Non-submission of the presentation slides results in a zero mark for all group members.
Group work reports and tutorial assessments will be weighted using Sparkplus
Independent Study: In order to complete assignments and to understand the concepts and applications presented, students will be required to engage in self-study, including in programming languages such as MATLAB.
Lecture Attendance: The expectation is maximum attendance in all lectures and tutorials, as the theoretical and practical elements of the tutorials support understanding in the lectures, labs and vice-versa. Attendance will be monitored through a register in lectures, which will be used to assess the % of lecture attendance over the full semester of individual students. Tutorials are not included in the register. Any non-attendance of lectures must be reported by the student to the UoSC ahead of the lecture, to allow a fair assessment of attendance. Evidence of the reason for non-attendance may be requested by the UoSC.
Final Presentation: the final presentation is a similar format to the tutorial sessions, including the assessment component and the individual mark, which will be based on the Sparkplus survey for the group. Non-attendance results in a zero mark for the individual. A pdf of the presentation must be uploaded to Canvas within an hour of the end of the final presentation session. Non-submission of the presentation slides results in a zero mark for all group members.
Participation: All group members must participate in any presentation assessment (interactive oral)

See note in 'Other relevant information' on Sparkplus survey requirement

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All compulsory readings for this unit can be accessed through Canvas.

Recommended texts and resources:

Measurement and instrumentation : theory and application, Morris, A.S., Langari, R., 2021, 3rd Edition, London, England : Academic Press (available online from USyd library)

Springer Handbook of Experimental Fluid Mechanics (2007)
Editors: Cameron Tropea, Alexander L. Yarin, John F. Foss

The Scientist and Engineer's Guide to Digital Signal Processing, by Steven W. Smith (available online - http://www.dspguide.com/pdfbook.htm)

Additional Online text resources will be supplied for private study as required throughout the course. It is also highly recommended the students complete background reading and practice of MATLAB as there is a strong programming component in the course. Online learning resources for MATLAB are available and should be studied by the students prior and during the course.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

LO1. investigate available literature to build up background information on set laboratory problems
LO2. understand concepts of aeronautical measurements including pressure, thermal, optical, structural and airborne measurands
LO3. apply scientific principles to a particular situation to obtain solutions to experimental problems
LO4. complete a set of group laboratory reports and an individual report on aspects of aeronautical measurements including pressure, thermal, optical, structural and airborne measurands
LO5. work as a team to efficiently manage a set of laboratory experiments, present solutions and produce results that meet set deadlines
LO6. present laboratory data and a group laboratory report or tutorial question to a group of colleagues
LO7. understand lab and health and safety requirements

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice.
	Engineers Australia Curriculum Performance Indicators - 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
	Engineers Australia Curriculum Performance Indicators - 4. ENGINEERING APPLICATION EXPERIENCE
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
	Engineers Australia Curriculum Performance Indicators - 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria.

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Additional worked examples and tutorial exercises have been developed for the unit and will be presented and discussed in the lectures and tutorials. The written assessment structure has been reduced to 1 individual report and 3 group reports

Additional information

For all 3 group reports (for Lab 3 - Lab 5) and for all assessed tutorial sessions, every student must complete a SparkPlus survey each time. The survey results will be used to weight a student's effort into an appropriate individual mark, from the overall report or presentation mark. The Lab 5 Sparkplus survey results will be used to weight individual marks from a group mark, for the final presentation.

The Sparkplus weighting is the main weighting tool, although any additional information that the UoSC has about the group performance may be used to adjust an individual weighting. If for any report or tutorial assessment, a student does not complete the Sparkplus survey, they will receive a zero individual mark for that specific report. Following release of individual marks, a student cannot retrospectively complete the corresponding Sparkplus survey.

Overall report and tutorial assessment marks will be released to each group each time. However, this is not the individual mark, which will appear when the survey results are complete. This may mean a delay between the release of the overall group mark and an individual mark.

Additional costs

N/A

Site visit guidelines

N/A

Work, health and safety

Students will be briefed on the health and safety requirements in the laboratories, ahead of attending the laboratory sesssions.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

AERO2710: Analysis of Aerospace Engineering Data

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links