Outline

Overview

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

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ENGG9801 or ENGG1801 or ENGG1810 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	Sue Chng, sue.chng@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final Exam Computer Examination	50%	Formal exam period	2 hours
Outcomes assessed:
Small continuous assessment	Revision sets A question set to check mastery of contents.	20%	Multiple weeks	15 minutes
Outcomes assessed:
Small test	Mid-semester test Formative assessment to check mastery of contents from W1-W5.	10%	Week 06	1 hour
Outcomes assessed:
Assignment	Assignment Programming and documentation.	20%	Week 12 Due date: 28 May 2021 at 23:59	2 weeks
Outcomes assessed:
= hurdle task ? = 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: edstem.org

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.

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.

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.

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 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.

Weekly schedule

WK	Topic	Learning activity
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)
Week 02	1. Control Flow: Selections II and Loops 1, 2. Lists	Lecture and tutorial (4 hr)
Week 03	1. Control Flow: Loops II, 2. Functions I 3. Vectors and Matrices	Lecture and tutorial (4 hr)
Week 04	1. Functions II 2. Introduction to standard packages	Lecture and tutorial (4 hr)
Week 05	1. File I/O 2. 2D Plots	Lecture and tutorial (4 hr)
Week 06	1. Debugging 2. Exception	Lecture and tutorial (4 hr)
Week 07	1.Classes and Objects 2. 3D Plots	Lecture and tutorial (4 hr)
Week 08	Testing 1	Lecture and tutorial (4 hr)
Week 09	Curve Fitting	Lecture and tutorial (4 hr)
Week 10	1. Numerical Methods 2. Simulation 1	Lecture and tutorial (4 hr)
Week 11	Simulation 2	Lecture and tutorial (4 hr)
Week 12	Testing 2	Lecture and tutorial (4 hr)
Week 13	Revision	Lecture and tutorial (4 hr)

Attendance and class requirements

Course websites:
The course website on edstem.org 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.

Attendance:
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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

This is the first time the unit is offered.

Additional information

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 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.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ENGG9810: Introduction to Engineering Computing

Semester 1, 2021 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect