Computer Networks

Unit code:NIT1104Study level:Undergraduate

Credit points

12
(Generally, 1 credit = 10 hours of classes and independent study.)

Location

Footscray Park
VU Brisbane
VU Sydney

Prerequisites

(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)

Overview

In this unit, you’ll explore the principles and practices of computer networks, focusing on data communication systems and network design. You will review how networking supports modern business and personal applications, and gain hands-on experience with technologies like TCP/IP, IP addressing, subnetting, and routing. Through lab sessions and design-based assessments, you will develop, implement, and troubleshoot IP and switching networks for real-world use. By the end of the unit, you will be ready to tackle networking challenges aligned with industry standards.

Learning Outcomes

On successful completion of this unit, students will be able to:

Review modern business and personal applications of data communication systems to demonstrate specialist computer networking skills and knowledge;
Apply various technologies to solving data communication and networking problems;
Design IP networks and subnetworks;
Design switching networks for a range of contemporary applications; and
Collaborate in the implementation of moderately complex networks consistent with industry standards.

Study as a single unit

This unit can be studied on its own, without enrolling in a full degree.

Learn more about single units of study at VU

Assessment

For Melbourne campuses

Assessment type: Laboratory Work | Grade: 15%

In-Class practical test

Assessment type: Test | Grade: 30%

In-class written test (problem solving & theory) with Q&A verification.

Assessment type: Project | Grade: 55%

Technical design report with demonstrations, presentation and oral Q&A. (Group)

Laboratory Work: Students complete practical networking tasks under supervision.

Test: Students complete real-time data analysis, coding, or modelling tasks under supervision. Execution is observed, and outcomes are verified through live checks or oral Q&A to ensure individual performance.

Project: Students analyse real data and produce findings in written form, supported by oral presentation or follow-up Q&A verifying interpretations and methods. AI-generated content is flagged via reflective process documentation.