Skip to main content
Unit of study_

ENGG9810: Introduction to Engineering Computing

Semester 1, 2021 [Normal day] - Remote

This unit is an essential starting point for engineers to learn the knowledge and skills of computer programming, using a procedural language.Crucial concepts include defining data types, control flow, iteration, and functions. Studentswill learn to translate a general engineering problem into a computer program. This unit trains students in the software development process, which includes programming, testing and debugging.

Unit details and rules

Unit code ENGG9810
Academic unit Computer Science
Credit points 6
ENGG9801 or ENGG1801 or ENGG1810 or INFO1110 or INFO1910 or INFO1103 or INFO1903 or INFO1105 or INFO1905 or COSC1003
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Sue Chng,
Type Description Weight Due Length
Final exam (Record+) Type B final exam hurdle task Final Exam
Computer Examination
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11
Small continuous assessment Revision sets
A question set to check mastery of contents.
20% Multiple weeks 15 minutes
Outcomes assessed: LO1 LO11 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Small test Mid-semester test
Formative assessment to check mastery of contents from W1-W5.
10% Week 06 1 hour
Outcomes assessed: LO1 LO11 LO8 LO7 LO5 LO4 LO3 LO2
Assignment Assignment
Programming and documentation.
20% Week 12
Due date: 28 May 2021 at 23:59
2 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11
hurdle task = hurdle task ?
Type B final exam = Type B final exam ?

Assessment summary

  • Assignment: Students will be required to produce a deployable solution  to a given engineering problem. 
  • Mid-semester test: This will be conducted during the tutorial class on Week 6. 
  • Revision sets: These must be completed individually and are administered through Ed Lessons. There will be different types of questions e.g. MCQ, short responses and programming problems, that assesses your understanding of the contents covered in the previous and current week. You will be required to complete them within the time limit. Revision sets are administered on W3, W5, W9 and W11. 
  • Final exam: Covers all aspects of the course. This is a closed book examination. You are not allowed to access online recources etc. 

Detailed information for each assessment can be found on the course website:

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.

For more information see

The tutor will provide feedback for the revision sets during the tutorial. It must be submitted by the due date for checking by teaching staff. 

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.

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:

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: Late penalty for any online assessment is 25% per day. It is a cap based penalty: 1 day late, maximum attainable mark is 75%. 2 days late, maximum attainable mark is 50%. 3 days late, maximum attainable mark is 25%.

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 to Python. 2. Basics - Data types, variables, operators and in-built functions 3. Control Flow - Selection I Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5
Week 02 1. Control Flow: Selections II and Loops 1, 2. Lists Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5
Week 03 1. Control Flow: Loops II, 2. Functions I 3. Vectors and Matrices Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5
Week 04 1. Functions II 2. Introduction to standard packages Lecture and tutorial (4 hr) LO4 LO7
Week 05 1. File I/O 2. 2D Plots Lecture and tutorial (4 hr) LO7 LO8 LO10 LO11
Week 06 1. Debugging 2. Exception Lecture and tutorial (4 hr) LO6
Week 07 1.Classes and Objects 2. 3D Plots Lecture and tutorial (4 hr) LO4 LO5 LO8 LO10 LO11
Week 08 Testing 1 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6
Week 09 Curve Fitting Lecture and tutorial (4 hr) LO1 LO2 LO3 LO9
Week 10 1. Numerical Methods 2. Simulation 1 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO8 LO9 LO11
Week 11 Simulation 2 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO8 LO9 LO11
Week 12 Testing 2 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6
Week 13 Revision Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11

Attendance and class requirements

Course websites:
The course website on will contain information, including important announcements. Teaching staff will be communicating to all students and it is considered part of the course. Students are expected to regularly visit this website to know these announcements and information concerning format and schedule of assessment. The Canvas site will be used for the publishing of results. 

Students are recommended to attend their tutorial class each week for feedback on assessments and learning. Students who are unable to attend a tutorial class will be responsible to catch up on the contents covered before the next tutorial class. 

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.

Reference books:

  • Robert Sedgewick, Kevin Wayne, Robert Dondero  – Introduction to Programming in Python: An Interdisciplinary Approach. Pearson Higher Ed USA, 2015. 9780134076430
  • Pine, David J. Introduction to Python for Science and Engineering . Boca Raton, FL: CRC Press, 2019. Web.
  • Wood, M. (2015). Python and Matplotlib essentials for scientists and engineers . Morgan & Claypool Publishers.

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. Develop programs to solve problems using computers.
  • LO2. Employ conventions for writing consistently readable code.
  • LO3. Compose a structured algorithmic design to solve a specified problem.
  • LO4. Apply fundamental programming principles including data types, variables and operators, flow-control: simple statements, sequences, if-then-else, loops, functions, input/output and arrays; to produce a program that solves a specified problem.
  • LO5. Compose, analyse, and trace procedural code to determine the expected output of a given program or produce a specified output.
  • LO6. Apply testing methods and assess programs through debugging with the ability to write a set of tests for a small program or function
  • LO7. Understand standard modules and packages in Python
  • LO8. Read and interpret different input formats to produce the desired outcome.
  • LO9. Apply basic numerical methods including numerical integration, curve fitting, root solving/optimisation and the least squares method
  • LO10. Write simple functions to perform computational methods including calculation of basic statistics, regression, correlation, searching, sorting on data.
  • LO11. Plot in two and three dimensions to produce an appropriate visualization of the data.

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.

This is the first time the unit is offered.

Every week students must:

  • Attend and take notes for the Live lecture (Mondays) OR watch and take notes for the Recorded lecture 
  • Complete the weekly Lesson for Lecture
  • Prepare for the Tutorial by reviewing reading, lecture and tutorial questions 
  • Attend and participate in weekly Tutorial with tutor (as timetabled)
  • Complete the weekly Lesson for Tutorial before the next tutorial commences

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.

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see, or the similarity report available in ED ( 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.


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.