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Algorithms
Master the logic and techniques behind problem-solving in computing.
Topics
Exam‑style questions with instant feedback.
Section 1: Introduction
Section 2: Complexity Analysis
Section 3: Sorting Algorithms
Section 4: Searching Algorithms
Section 5: Data Structures
Section 6: Divide and Conquer
Section 7: Greedy Methods
Section 8: Dynamic Programming
Section 9: Graph Algorithms
Section 10: String Algorithms
Section 11: NP-Completeness
Section 12: Algorithmic Strategies
What you’ll achieve
Learn core algorithms such as sorting, searching, and graph traversal.
Understand algorithm complexity and Big-O performance analysis.
Explore dynamic programming and optimisation strategies.
Gain skills in designing efficient and scalable problem-solving methods.
Build a foundation for advanced study in AI, data science, and systems design.
Course overview
Everything you need to excel in Algorithms
Algorithms form the backbone of computer science, providing step-by-step procedures for solving computational problems efficiently. This course introduces fundamental algorithmic techniques, including sorting, searching, graph algorithms, dynamic programming, and greedy strategies. Students will analyse algorithm complexity using Big-O notation and learn how to design solutions that optimise performance. With applications across software development, data science, and artificial intelligence, algorithms are essential for building scalable and effective systems.
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