Algorithm analysis. Application and implementation of some common data structures: stacks, queues, lists, priority queues, tables, sets and collections. Data representations including: arrays, linked lists, heaps, trees (including balanced trees) and hashing. Design of application programs making use of common data structures. Design and implementation of new data structures. Study of advanced algorithms in areas such as: graph theory, pattern searching and data compression. Access to the University's computer systems through an Internet service provider is compulsory for off-campus students.
At the completion of this unit students will have -
- the ability to analyse simple algorithms to work out an order of magnitude estimate of running time and space;
- familiarity with some of the most common data structures: stacks, queues, lists, priority queues, tables, sets, collections;
- the ability to implement these data structures using various common data representations: arrays, linked lists, heaps, trees (including balanced trees), hashing;
- the ability to evaluate which implementation would be most appropriate for a given data structure and application;
- the ability to apply the same principles used in implementing the common data structures to implement other data structures;
- ability to design and implement new data structures;
- an understanding of some more advanced algorithms in areas such as: graph theory (shortest path etc), pattern searching, data compression (precise selection of advanced algorithms will vary from year to year);
- the ability to design new algorithms to solve new problems;
- an enjoyment of programming as an intellectual exercise;
- an appreciation of the elegance of certain data structures and algorithms as a form of art;
- an interest in understanding how data structures and algorithms are implemented rather than merely using other peoples implementations (and consequently a preference for open source software.
Examination (3 hours): 60%; In-semester assessment: 40%
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
- One 2-hour lecture
- One 2-hour laboratory
(b.) Study schedule for off-campus students:
- Off-campus students generally do not attend lecture and tutorial/laboratory sessions, however should plan to spend equivalent time working through the relevant resources and participating in discussion groups each week.
(c.) Additional requirements (all students):
- a minimum of 8 hours of independent study in some weeks for completing lab and project work, private study and revision.
FIT1007 or GCO1812 or GCO9808 or FIT2034
FIT2004, FIT2071, FIT9015, GCO2817, GCO3512, GCO9807