Algorithms and Data Structures form the foundation of efficient programming and computational problem-solving. This course provides a structured learning journey from algorithm analysis and essential data structures to practical problem-solving and dynamic programming. It is designed to help learners understand not only how common techniques work but also how to evaluate and apply them when approaching programming challenges.

The course begins with algorithm analysis, introducing asymptotic notation and the principles used to describe computational efficiency. Learners explore time complexity alongside Big O, Big Omega and Big Theta notation, developing the ability to reason about how algorithms behave as input sizes increase. Dedicated cost-analysis exercises provide opportunities to reinforce these concepts through practical evaluation.

The next stage focuses on key data structures. Learners examine dictionaries, including their implementation, iteration techniques and fundamental methods, before applying their knowledge to solved problems such as a casino simulation and a Bag of Words algorithm. Stacks are introduced through implementation concepts and problem-solving exercises involving palindromic sequences and parenthesisation evaluation.

The course also explores vectors, their implementation and commonly used methods. Practical problems demonstrate how these structures can be applied to tasks such as finding minimum values, identifying identical elements and reversing sequences. These exercises encourage learners to translate theoretical knowledge into systematic solutions.

The final section introduces dynamic programming as an approach to solving optimisation and overlapping subproblem challenges. Through examples including the Fibonacci sequence and the weighted activity selection problem, learners explore implementation guidelines and develop a clearer understanding of how complex problems can be broken into manageable stages.

By completing the course, learners can strengthen their analytical thinking, improve their ability to assess algorithm efficiency and develop a more organised approach to solving programming problems. These skills provide a valuable foundation for technical interviews, further computer science study and more advanced software development topics.