To study this module you will need the learning equivalent to the module listed or have passed this module. An examples of equivalent learning include experience of programming in a suitable high level programming language.

Performance analysis and approximations for algorithms will be considered. The big “O” notation will be looked at to estimate space and time requirements for some classical algorithms and data structures. This will include the partition of problems into constant, linear and exponential growth properties of the associated algorithms. Iterative and recursive algorithms and their relationships will form a core of investigating various searching and sorting methods.
The usual useful linear data structures such Arrays, Lists, Queues and Stacks, their capabilities, performance and uses will be studied. Several associative data structures such as Sets, Dictionaries by Hash Tables and Binary Trees will be analysed to evaluate their capabilities and performance characteristics.

Upon completion of this module you will be able to
LO1: Evaluate the performance of an algorithm based on the size of a problem input
LO2: Examine and evaluate the algorithms that work on various data structures
LO3: Evaluate and discuss the capabilities of data structures to applicable problems
LO4: Demonstrate a working knowledge of a relevant data structures API
LO5: Design, develop and evaluate data structures and algorithms
LO6: Evaluate and design encoding algorithms, including cryptographic, hashing, and compression