The Essential Mathematics for Computer Science specialisation provides the fundamental toolkit needed to excel in computing, data science, and software engineering. Across five courses, you will progress from core mathematical foundations to advanced topics that underpin modern computer science. You’ll begin with sets, number systems, functions, and relations, move into advanced mathematical methods such as algebra, vectors, combinatorics, and probability, then explore geometry, trigonometry, and calculus for modelling motion and change. The pathway continues with logic and reasoning, where you’ll master propositional and predicate logic, Boolean algebra, and proof strategies, before concluding with algorithms and complexity, analysing efficiency, recursion, and computational limits.
By completing this specialisation, you will gain industry-relevant skills in discrete mathematics, logic, algebra, calculus, probability, and algorithmic reasoning. These competencies prepare you to design efficient algorithms, analyse complexity, reason formally, and apply mathematical methods to real computing challenges. Whether your goal is to pursue further study in computer science, strengthen your programming and problem-solving skills, or advance in fields such as data science, artificial intelligence, or systems design, this specialisation ensures you have the rigorous mathematical foundation that employers and universities expect.
Applied Learning Project
Hands-On Learning Overview
Throughout this specialization, you will learn through a variety of interactive activities designed to deepen understanding and engagement. These include hands-on labs, game-like environments, and AI-driven dialogues that let you experiment, explore, and reason through mathematical problems in a dynamic and immersive way.
Later modules guide you through applying algebra, probability, combinatorics, trigonometry, calculus, logic, and Boolean algebra to solve structured problems, analyse scenarios, and verify correctness. You will analyse algorithms, assess efficiency, and reason about complexity, just as computer scientists do in practice. By participating in this mix of labs, games, and AI interactions, you will develop both a deep understanding of theoretical concepts and the practical skills to better solve computational problems.
