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Unit of study_

ENGG9801: Engineering Computing

Intensive February, 2021 [Normal day] - Remote

This unit introduces students to solving engineering problems using computers. Students learn how to organize data to present and understand it better using a spreadsheet (Excel), and also how to instruct the computer exactly what to do to solve complex problems using programming (Matlab). Real engineering examples, applications and case-studies are given, and students are required to think creatively and solve problems using computer tools. Matlab will cover three-quarters of the unit. The remaining one-quarter will be devoted to the use of Excel in engineering scenarios. Furthermore, cross integration between Matlab and Excel will also be highlighted. No programming experience is required or assumed. Students are assumed to have a basic understanding of mathematics and logic, and very elementary computing skills.

Unit details and rules

Unit code ENGG9801
Academic unit Computer Science
Credit points 6
ENGG5801 OR ENGG1801
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Sue Chng,
Lecturer(s) Amrit Sethi,
Tutor(s) Steve Kraynov,
Jasneil Singh,
Rhys Michelis,
Bryan Lim,
Hossein Moeinzadeh,
Type Description Weight Due Length
Small test Lab Test 1
Online in-class assessment during tutorial time on Week 3.
20% - 2 hours
Outcomes assessed: LO1 LO7 LO6 LO4 LO3
Small test Lab Test 2
Online in-class assessment during tutorial time on Week 5.
20% - 2 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Final exam (Open book) Type C final exam hurdle task Final Exam
Online exam held on 22 February 2021, 10am to 12pm.
55% Week 06
Due date: 22 Feb 2021 at 10:00

Closing date: 22 Feb 2021
2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment hurdle task Lab exercises
Submitted at the end of each tutorial session.
5% Weekly 2 hours/tutorial session
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
hurdle task = hurdle task ?
Type C final exam = Type C final exam ?

Assessment summary

  • Lab exercises: Completed lab exercises are to be submitted right after each tutorial. This component is worth 5% with 10 assessed labs, each lab equally weighted at 0.5%. The mark awarded for each lab will be awarded on a pass/fail.
  • Lab Test 1: This would be an online in-class assessment, taking place during tutorial time on Week 3. The lab exam will cover Matlab related concepts from days 1-5.
  • Lab Test 2: This would be an online in-class assessment, taking place during tutorial time on Week 5. The lab exam will cover Matlab related concepts from days 6-10, although material from days 1-5 would be assumed knowledge.
  • Final exam: Excel and Matlab related concepts are covered with most questions being Matlab-based. This exam would be conducted online on Week 6.Students must score at least 40% in the final exam to pass the unit (see Pass requirements)

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

Minimum Pass Requirement

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement
applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a
maximum final mark of no more than 45 regardless of their average.

For more information see guide to grades.

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.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 1. Introduction. 2. Excel basics, functions. Online class (4 hr) LO1 LO6 LO7
1. Matlab basics. 2. If functions, arrays Online class (4 hr) LO1 LO6 LO7
Loops Online class (4 hr) LO1 LO6 LO7
Week 02 Functions Basics Online class (4 hr) LO1 LO6 LO7
Functions - creating own functions Online class (4 hr) LO1 LO6 LO7
Week 03 1. Character strings 2.Text and file I/O, Online class (4 hr) LO6 LO7
Matrix algebra Online class (4 hr) LO3 LO6
Week 04 1. Images, 2.Movies Online class (4 hr) LO2 LO3 LO4 LO5
2-D and 3-D plotting, surface plots Online class (4 hr) LO2 LO4 LO5
Curve fitting Online class (4 hr) LO2 LO3 LO4 LO5
Week 05 Help for the final exam Online class (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

Attendance and class requirements

Attendance for lectures and tutorial sessions are compulsory.

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 for this unit can be accessed through the Library eReserve, available on Canvas.

  • David Smith, Engineering Computing with Matlab. Pearson Addison-Wesley, 2008.
  • Bernard Liengme, Guide to Microsoft Excel 2007 for Scientists and Engineers. Elsevier, 2008. 978-0-12-374623-8.

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. employ good practices in developing MATLAB and EXCEL applications and be aware of requirements for software benchmarking and validation
  • LO2. examine digital images represented as matrices and operations on images abstracted as operations on matrices. Be aware of how imaging software products are based on matrix operations
  • LO3. carry out simple matrix computations including matrix sum, product, dot product, calculating the determinant and elementary functions on matrix
  • LO4. evaluate data in Matlab from and in different formats and to interpret and process the data to obtain meaningful results. Able to plot data in 2 dimensions and use Matlab’s advanced 3-dimensional surface plots
  • LO5. identify the appropriate product for the particular class of engineering
  • LO6. reflect on basic concepts of computing such as abstraction, describing a solution of a problem as an algorithm and running Matlab programs. Ability to use MATLAB and EXCEL to model engineering problems
  • LO7. demonstrate fundamental programming concepts such as flow of control, loops, functions and parameters passing. Able to use basic data structures such as arrays and structures of heterogeneous objects.

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

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

Changes were made to adapt the class for online mode during the intensive semester.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

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.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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