Outline

Overview

This unit introduces students to computational thinking and programming skills essential for solving a variety of engineering problems. Students will explore how complex engineering applications can be modeled and analyzed using computational tools, including data processing and time-series analysis, dynamic system simulations, and decision-making optimization. The unit covers fundamental programming concepts鈥攕uch as variables, functions, data structures, algorithms, and data visualization鈥攁s well as essential coding skills. Students will learn to transform real-world engineering challenges into computational tasks, develop efficient programs to solve these tasks, and evaluate the effectiveness of their solutions through visualized results. Throughout the unit, students will engage in exemplary projects, such as analyzing brain functionality, simulating climate change, and planning robot motion.

Unit details and rules

Academic unit	Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ENGG1801 or INFO1110 or INFO1910 or INFO1103 or INFO1903 or INFO1105 or INFO1905 or COSC1003
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Rex di Bona, rex.dibona@sydney.edu.au
Lecturer(s)	Mohammad Saadatfar, mohammad.saadatfar@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Closed Book Supervised Exam	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Practical skill	Code tracing, debugging, AI critique, and reflection In-class engineering computing activities: weeks 2-12	20%	Multiple weeks	N/A	AI allowed
Outcomes assessed:
Out-of-class quiz	Early Feedback Quiz Questions to check mastery of concepts.	0%	Week 03 Due date: 29 Aug 2026 at 23:59	30 minutes	AI allowed
Outcomes assessed:
Experimental design	Project 1 Individual problem framing and Python foundations task with testing, validation and AI log.	10%	Week 05 Due date: 06 Sep 2026 at 23:59	2 weeks	AI allowed
Outcomes assessed:
Experimental design	Project 2 Group engineering computing project involving problem framing, implementation, data or algorithm development, testing and validation, with individual accountability.	30%	Week 10 Due date: 18 Oct 2026 at 23:59	4 weeks	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 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

Early Feedback Quiz, 0%: This online quiz will take place in Week 3 and cover key concepts from Weeks 1 to 3. It will provide early feedback on your understanding and readiness for the remaining unit activities.

In Class Engineering Computing Activities, 20%: These activities will be completed across Weeks 2 to 12 and will include code tracing, debugging, testing, solution comparison, AI critique and short reflections. The activities will assess your progressive understanding and application of the unit content.

Project 1, 10%: This individual project will be released in Week 3 and submitted in Week 5. It will assess engineering problem framing, Python foundations, implementation, testing, validation and appropriate documentation of any permitted AI use.

Project 2, 30%: This group project will be released in Week 6 and submitted in Week 10. Students will complete an engineering computing project involving problem framing, implementation, data or algorithm development, testing and validation. Individual accountability will form part of the assessment.

Final Examination, 40%: The final examination will assess all aspects of the unit, including problem framing, code interpretation, algorithm development, debugging, validation and critical evaluation of computational results. It will be a secure, closed book, supervised examination. AI tools and other unauthorised resources will not be permitted.

To pass this unit, a student must achieve at least 40% in the final examination and an overall final mark of 50 or more. A student who does not meet both requirements may receive a maximum final mark of 45, regardless of their calculated average.

Assessment criteria

The University awards common result grades, set out in the听听(Schedule 1).

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听Academic Integrity Policy.

Visit the听Current Students website听for more information on AI in assessments, including听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:

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.

Academic integrity

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

Our website听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听Academic Honesty Education Module (AHEM)听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听Current Students website听for more information on AI in assessments, including听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听simple extension.鈥� 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鈥檛 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听special consideration or special arrangements.

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

Using AI responsibly

Co-created with students,听听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听reflects the University鈥檚 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
Week 01	Engineering problems, computational thinking and responsible AI use	Lecture (2 hr)
Week 01	Tutorial	Tutorial (2 hr)
Week 02	Fundamentals of Programming (I): Python foundations through engineering calculations	Lecture (2 hr)
Week 02	Tutorial	Tutorial (2 hr)
Week 03	Fundamentals of Programming (II): decisions, iteration and early AI literacy	Lecture (2 hr)
Week 03	Tutorial	Tutorial (2 hr)
Week 04	Fundamentals of Programming (III): functions, decomposition and debugging	Lecture (2 hr)
Week 04	Tutorial	Tutorial (2 hr)
Week 05	Fundamentals of Programming (IV): lists, arrays, dictionaries, plotting and validation	Lecture (2 hr)
Week 05	Tutorial	Tutorial (2 hr)
Week 06	Engineering data, arrays and visualisation	Lecture (2 hr)
Week 06	Tutorial	Tutorial (2 hr)
Week 07	Data interpretation and AI critique	Lecture (2 hr)
Week 07	Tutorial	Tutorial (2 hr)
Week 08	Using AI to develop and improve computational solutions	Lecture (2 hr)
Week 08	Tutorial	Tutorial (2 hr)
Week 09	Algorithms, computational search and sensitivity analysis	Lecture (2 hr)
Week 09	Tutorial	Tutorial (2 hr)
Week 10	Validating computational results and understanding limitations	Lecture (2 hr)
Week 10	Tutorial	Tutorial (2 hr)
Week 11	Simulation of engineering systems	Lecture (2 hr)
Week 11	Tutorial	Tutorial (2 hr)
Week 12	Simulation of engineering systems: numerical errors and sensitivity to assumptions	Lecture (2 hr)
Week 12	Tutorial	Tutorial (2 hr)
Week 13	Unit revision and final assessment preparation	Lecture (2 hr)
Week 13	Tutorial	Tutorial (2 hr)
Weekly	6 hours of independent study each week for reviewing course material, independent research, and practicing programming skills.	Self-directed learning (78 hr)

Attendance and class requirements

Course websites:

The course website on Canvas will contain information, including important announcements. Teaching staff will communicate with all students via Canvas which is considered part of the course. Students are expected to regularly visit Canvas to know these announcements and information听concerning the format and schedule of assessment. Learning materials and assessments will also be stored at the course site on edstem.org/.听

听

Attendance

Students are expected to attend weekly lectures and tutorials and to participate actively in problem solving, coding, testing and discussion activities. Students who cannot attend a class are responsible for reviewing the learning materials and completing the associated activities before the following week.

Canvas is the primary source of unit information, announcements, learning materials and assessment instructions. Additional coding activities and submissions may be managed through Ed.

Some assessed in class activities will be completed during scheduled classes. Details of any participation or submission requirements will be provided through Canvas.

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 readings and learning resources required for this unit will be provided through Canvas or can be accessed through the University Library.

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. Understanding and framing engineering problems as computational tasks, including inputs, outputs, assumptions, constraints and validation criteria.
LO2. Apply core Python constructs, including variables, data types, control flow, functions, arrays and simple data structures, to solve defined engineering problems.
LO3. Develop Python programs that process, analyse and visualise engineering data.
LO4. Implement and test simple algorithms to support engineering decisions.
LO5. Critically evaluate computational and AI assisted outputs by checking correctness, limitations, assumptions and implications for engineering practice.

Graduate qualities

The graduate qualities are the qualities and skills that all 爆料王 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鈥檚 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 - 2. IN-DEPTH TECHNICAL COMPETENCE 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 2.3. Meaningful engagement with current technical and professional practices and issues in the designated field. 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity.
	Engineers Australia Curriculum Performance Indicators - 2. IN-DEPTH TECHNICAL COMPETENCE 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity. 5. PRACTICAL AND 鈥楬ANDS-ON鈥� EXPERIENCE
	Engineers Australia Curriculum Performance Indicators - 1. ENABLING SKILLS AND KNOWLEDGE DEVELOPMENT 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 2. IN-DEPTH TECHNICAL COMPETENCE 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance.
	Engineers Australia Curriculum Performance Indicators - 2. IN-DEPTH TECHNICAL COMPETENCE 2.2. Application of enabling skills and knowledge to problem solution in these technical domains.
	Engineers Australia Curriculum Performance Indicators - 2. IN-DEPTH TECHNICAL COMPETENCE 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity.

Engineers Australia Curriculum Performance Indicators -

Competency code	Taught, Practiced or Assessed	Competency standard
1.1		Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice.
1.2		Tackling technically challenging problems from first principles.
2.1		Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks.
2.2		Application of enabling skills and knowledge to problem solution in these technical domains.
2.3		Meaningful engagement with current technical and professional practices and issues in the designated field.
2.4		Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity.
4.2		Ability to use a systems approach to complex problems, and to design and operational performance.
5.1		An appreciation of the scientific method, the need for rigour and a sound theoretical basis.
5.5		Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.
5.6		Skills in the design and conduct of experiments and measurements.

Responding to student feedback

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

The lecture plan, assessment, and delivery have all been revised to improve learning experiences, based on class feedback.

Additional information

Every week students must:

Attend听and听take notes听for the听Live lecture听(Mondays) or watch听and听take notes听for the听Recorded lecture听
Prepare听for the Tutorial听by reviewing reading, lecture and lab questions听
Attend听and听participate听in听weekly Tutorial听with tutor (as timetabled)

听

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/), or the similarity report available in ED (edstem.org). These programs work in a similar way to Turnitin in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Work, health and safety

Students must follow University work health and safety requirements when using teaching spaces and computing facilities. Students should maintain a safe workstation, take appropriate breaks during extended computer use, and promptly report equipment faults, hazards or accessibility concerns to teaching staff.

Students must not attempt to access, modify or interfere with University computer systems, networks or accounts without authorisation.

Disclaimer

Important: the 爆料王 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 .

爆料王

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

Work, health and safety

Disclaimer

On this page

Useful links

ENGG1810: Introduction to Engineering Computing

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Unit details and rules

Teaching staff

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Additional information

Work, health and safety

Disclaimer

On this page

Useful links