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B.S in Computing

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A sample of courses required for the B.S in Computing is presented below. A total of 124 credit hours are required for successful completion of this programme.

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  • CPTR120: Introduction to Computer Programming
    Introduction to Computer Programming discusses the history, architecture and function of computer hardware and software including networks, data and instruction representation and data organisation. The course introduces problem solving methods and algorithm design using the logic control structures of sequence, selection and iteration and is also an introduction to application development using a selected programming language. It also introduces the student to problem solving, algorithm development and documentation techniques, the concepts of structured programming and design correctness, data types, control structures, arrays and functions.

  • MATH141: Calculus I
    Calculus l is the study of functions, limits, continuity, derivatives and the applications of derivatives and integrals.

  • CPTR441: Computer Graphics
    An introduction to computer graphics - an important foundation for rendering and animation. The course examines the multimedia aspects of the World Wide Web, design of human-computer interfaces and investigates the principles, techniques and tools for multimedia, visual modelling and virtual reality. Students are exposed to the foundational mathematics involved in graphic rendering algorithms.

  • MATH182: Calculus with Applications
    Calculus with Applications is an introduction to one variable calculus, to include techniques for finding maxima and minima, as well as partial derivatives. These concepts are used to solve case studies drawn from the areas of business and social sciences.

  • STAT340: Probability Theory with Statistical Applications
    Probability Theory with Statistical Applications deals with the basic concepts of probability theory and statistics. This course includes definitions of probability, random variables, probability, distributions, estimators, and statistical decision theory. This course is tailored for students with a background in calculus and algebra who desire a deeper understanding of the applicable statistical methods.