This section of the University of Cambridge Algorithms course continues building a strong foundation in Data Structures and Algorithms. It focuses on understanding how fundamental algorithms work and how their performance is analyzed.
You will start with insertion sort, learning how elements are inserted into their correct position step by step. This helps build intuition for how simple sorting algorithms operate.
Next, the course explores asymptotic complexity in depth, including Big-O, Big-Theta, Big-Omega, as well as small-o and small-omega notation. These concepts are essential for measuring algorithm efficiency and understanding scalability.
You will also study the cost of sorting, which explains why some algorithms perform better than others depending on input size and structure.
The course then compares basic sorting algorithms such as selection sort, binary insertion sort, and bubble sort, showing their differences in performance and behavior.
More advanced topics include merge sort and heap sort. These algorithms introduce more efficient strategies for sorting large datasets using divide-and-conquer and heap-based structures.
Finally, the course explains heaps and their importance in algorithm design, including why they are efficient for building and managing structu
