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

NOV12-CLAYTON-ON-CAMPUS

S1-01-CLAYTON-ON-CAMPUS

S1-01-MALAYSIA-ON-CAMPUS

S1-FF-CLAYTON-FLEXIBLE

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: CSE1303, CSC1030, FIT1015, FIT2085

Contacts

Chief Examiner(s)

Professor Maria Garcia De La Banda

Dr Pierre Le Bodic

Unit Coordinator(s)

Dr Muhammad Fermi Pasha

Notes

IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.

Teaching approach

Peer assisted learning

Assessment

1 - In-semester assessment

2 - Examination (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

Technology resources

Availability in areas of study

Computer science
Computational science