units

FIT2009

Faculty of Information Technology

This unit entry is for students who completed this unit in 2016 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

# FIT2009 - Data structures and algorithms

## 6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Information Technology

Offered

South Africa

• Second semester 2016 (Day)

### Synopsis

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.

### Outcomes

At the completion of this unit, students should be able to:

1. analyse simple algorithms to work out an order of magnitude estimate of running time and space;
2. describe and implement the most common data structures: stacks, queues, lists, priority queues, tables, sets, collections using various common data representations: arrays, linked lists, heaps, trees (including balanced trees), hashing;
3. evaluate which implementation would be most appropriate for a given data structure and application;
4. apply the same principles used in implementing the common data structures to implement other data structures and design and implement new data structures;
5. describe 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);
6. design new algorithms to solve new problems.

### Assessment

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.

• a minimum of 8 hours of independent study in some weeks for completing lab and project work, private study and revision.