FIT1008 - Introduction to computer science

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Overview

This unit introduces students to core problem-solving, analytical skills, and methodologies useful for developing flexible, robust, and maintainable software. In doing this it covers a range of conceptual levels, from high-level algorithms and data-structures, down to the machine models and simple assembly language programming. Topics include data types; data structures; … For more content click the Read More button below.

This unit introduces students to core problem-solving, analytical skills, and methodologies useful for developing flexible, robust, and maintainable software. In doing this it covers a range of conceptual levels, from high-level algorithms and data-structures, down to the machine models and simple assembly language programming. Topics include data types; data structures; algorithms; algorithmic complexity; recursion; and translation to assembly language.

Offerings

OCT12-CLAYTON-ON-CAMPUS

S1-01-CLAYTON-ON-CAMPUS

S1-01-MALAYSIA-ON-CAMPUS

S2-01-CLAYTON-ON-CAMPUS

S2-01-MALAYSIA-ON-CAMPUS

Rules

Enrolment Rule

Prerequisites: ((One of FIT1040, ECE2071, FIT1002) and FIT1029) or FIT1045 or FIT1053
Students beginning FIT1008 are assumed to be able to: Identify the main components of an algorithm (variables, operators, expressions, etc), and write the algorithm associated to the specification of a simple problem. Be able to translate a simple algorithm into a program containing variable declarations, selection, repetition, and lists and/or arrays.
Prohibitions: FIT1015, FIT1054, FIT2085

Contacts

Chief Examiner(s)

Dr Pierre Le Bodic

Unit Coordinator(s)

Dr Muhammad Fermi Pasha

Learning outcomes

On successful completion of this unit, you should be able to:

Translate simple problem statements into algorithms, implement them in a high level programming language and test them;

Summarise and compare the properties of basic abstract data types such as stacks, queues, lists, trees, priority queues, heaps and hash tables;

Evaluate different algorithms and implementations of basic abstract data types;

Analyse algorithms by determining their best case and worst case big O time complexity;

Deconstruct simple high-level code into assembly code such as MIPS R20

Teaching approach

Peer assisted learning

Assessment summary

This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Interview Practicals

2 - Solo/Colab Pracs

3 - Scheduled final assessment (3 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Laboratories

Tutorials

Workshops

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Learning resources

Recommended resources

Technology resources

Availability in areas of study

Computer science
Computational science