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Unit outline_

ENGG1801: Engineering Computing

Intensive January - February, 2021 [Normal day] - Remote

This unit introduces students to solving engineering problems using computers. Students learn how to organise 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

Academic unit Computer Science
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
COSC1003
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Sue Chng, sue.chng@sydney.edu.au
Lecturer(s) Amrit Sethi, amrit.sethi@sydney.edu.au
Tutor(s) Jasneil Singh, jasneil.singh@sydney.edu.au
Steve Kraynov, steve.kraynov@sydney.edu.au
Bryan Lim, bryan.lim@sydney.edu.au
Rhys Michelis, rmic5609@uni.sydney.edu.au
Hossein Moeinzadeh, hossein.moeinzadeh@sydney.edu.au
Type Description Weight Due Length
Assignment hurdle task Lab Exercises
Submitted at the end of each tutorial session.
5% - 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Small test Lab Test 1
Online in-class assessment during tutorial time on Week 3.
20% Week 03 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% Week 05 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
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
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

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

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

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 statements; 3. 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
1. 2D and 3D plotting; 2. Surface plots Online class (4 hr) LO2 LO4 LO5
Curve fitting Online class (4 hr) LO2 LO3 LO4 LO5
Week 05 Review & Final Exam Help 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

  • 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 demonstrate awareness of the requirements for software benchmarking and validation
  • LO2. examine digital images represented as matrices and operations on images abstracted as operations on matrices, and demonstrate awareness of how imaging software products are based on matrix operations
  • LO3. demonstrate ability to carry out simple matrix computations including matrix sum, product, dot product, calculating the determinant and elementary functions on matrix
  • LO4. identify the appropriate product for the particular class of engineering
  • LO5. evaluate data in MATLAB from and in different formats, interpret and process the data to obtain meaningful results, show ability to plot data in two dimensions and use MATLAB’s advanced three dimensional surface plots
  • LO6. reflect on basic concepts of computing such as abstraction, describing a solution of a problem as an algorithm, run MATLAB programs and demonstrate ability in using MATLAB and Excel to model engineering problems
  • LO7. demonstrate fundamental programming concepts such as flow of control, loops, functions and parameters passing, and demonstrate ability 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.
 

Disclaimer

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.