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What you'll be able to do (official learning outcomes)
explain the use of big-O, omega, and theta notation to describe the amount of work done by an algorithm
use big-O, omega, and theta notation to give asymptotic upper, lower, and tight bounds on time and space complexity of algorithms
determine the time and space complexity of simple algorithms
deduce recurrence relations that describe the time complexity of recursively defined algorithms
solve elementary recurrence relations
use pattern matching to analyse substrings, and
Course contents
Basic algorithmic analysis. Asymptotic analysis of Upper and average complexity bounds. Standard Complexity Classes. Time and space trade-offs in analysis recursive algorithms. Algorithmic Strategies. Fundamental computing algorithms. Numerical algorithms. Sequential and Binary search algorithms. Sorting algorithms, Binary Search trees.
Source: the Nigerian Universities Commission (NUC) Core Curriculum and Minimum Academic Standards (CCMAS). StudyOps loads this syllabus automatically when you study CSC 401.