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Unit outline_

ENGG1801: Engineering Computing

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

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 Xi Wu, xi.wu@sydney.edu.au
Lecturer(s) Sue Chng, sue.chng@sydney.edu.au
Xi Wu, xi.wu@sydney.edu.au
Type Description Weight Due Length
Final exam hurdle task Online Final exam
Online Final Exam. TBA: 1 to 1.5 hours
50% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Assignment 1
Excel Assignment
5% Week 03 n/a
Outcomes assessed: LO1 LO6 LO7
Assignment Assignment 2
Matlab Assignment
15% Week 08 n/a
Outcomes assessed: LO3 LO5 LO6 LO7
Assignment Assignment 3
Matlab Assignment
25% Week 11 n/a
Outcomes assessed: LO2 LO3 LO4 LO5 LO6 LO7
Assignment Lab exercises
Submission on canvas
5% Weekly n/a
Outcomes assessed: LO6 LO7
hurdle task = hurdle task ?

Assessment summary

  • Lab exercises: Programming tasks done in weekly tutorials.
  • Assignments: Practical programing assignments.
  • Final exam: Online exam.

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

​In order to pass this unit, a student must achieve at least 40% in the final examination. 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 Lecture and tutorial (4 hr) LO1 LO6 LO7
Week 02 1. Functions; 2. Plotting; 3. Solving equations; 4. File I/O Lecture and tutorial (4 hr) LO1 LO6 LO7
Week 03 Matrix algebra Lecture and tutorial (4 hr) LO3 LO6
Week 04 1. Matlab basics; 2. If statements; 3. Arrays Lecture and tutorial (4 hr) LO1 LO6 LO7
Week 05 Loops Online class (4 hr) LO1 LO6 LO7
Week 06 Functions Online class (4 hr) LO1 LO6 LO7
Week 07 Functions Online class (4 hr) LO1 LO6 LO7
Week 08 Matrix algebra Online class (4 hr) LO2 LO3
Week 09 1. Images; 2. Movies Online class (4 hr) LO2 LO3 LO4 LO5
Week 10 1. 2D and 3D plotting; 2. Surface plots Online class (4 hr) LO2 LO4 LO5
Week 11 1. Text and file I/O; 2. Character strings Online class (4 hr) LO6 LO7
Week 12 1. Interpolation and curve fitting Online class (4 hr) LO2 LO3 LO4 LO5
Week 13 Final exam help Online class (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

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 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.

No changes have been made since this unit was last offered

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.