FIT2085 - Introduction to computer science for engineers

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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 … 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

S1-01-CLAYTON-ON-CAMPUS

S1-01-MALAYSIA-ON-CAMPUS

S2-01-CLAYTON-ON-CAMPUS

S2-01-MALAYSIA-ON-CAMPUS

Requisites

Prerequisite

Prohibition

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 problem statements into algorithms and implement them in a high level programming language;

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

Theoretically and experimentally evaluate different implementations of basic abstract data types;

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

Implement and evaluate different strategies for solving a given problem;

Recognise the limitations - both theoretical and practical - of algorithms;

De-construct 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 Pracs

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

For algorithms and data structures:

B. Miller and D. Ranum - Problem solving with algorithms and data structures using Python.
M.T. Goodrich, R. Tamassia, M.H. Goldwasser - Data Structures and Algorithms in Python

For MIPS

R.L. Britton - MIPS Assembly Language Programming

For Python:

Dive into Python 3: http://getpython3.com/diveintopython3/
Python 3 tutorial: http://docs.python.org/3/tutorial/
Interactive Python: http://interactivepython.org/runestone/static/thinkcspy/index.html
Algorithms in Python: http://interactivepython.org/runestone/static/pythonds/index.html

Technology resources

Availability in areas of study

Software engineering