Module Artifacts · 8 items
A structured outline of the module's topics covering sorting algorithm fundamentals (definitions, terminology), and the concepts, step-by-step logic, and JavaScript implementations of Bubble Sort, Insertion Sort, and Merge Sort, used by learners and instructors to navigate the module's content and sequence.
A narrative introduction to sorting algorithms covering why sorting matters, its connection to core algorithm design concepts such as complexity analysis and memory management, and how learners should approach the module as a foundation for the specific algorithms that follow.
An explanation of how bubble sort works — repeatedly comparing and swapping adjacent elements — including its step-by-step logic, best- and worst-case time complexity, and why it is used as a teaching example despite being impractical for production code.
A walkthrough of insertion sort using the playing-card analogy, describing how the algorithm maintains a growing sorted region by shifting elements rightward to insert each new item in its correct position, along with its time complexity and practical use cases.
A detailed explanation of merge sort's divide-and-conquer strategy — recursively splitting an array into halves, sorting each half independently, and merging the results — covering its time and space complexity and why it is considered reliable for large datasets.
A coverage of quicksort's pivot-based divide-and-conquer approach introduced by Tony Hoare, explaining the partitioning process, pivot selection strategies, recursive structure, and average versus worst-case time complexity.
A comparative analysis of bubble sort, insertion sort, merge sort, and quicksort across time complexity, space usage, and stability, intended to help learners and instructors evaluate which algorithm is most appropriate for a given scenario.
A comparative analysis of bubble sort, insertion sort, merge sort, and quicksort across time complexity, space usage, and stability, intended to help learners and instructors evaluate which algorithm is most appropriate for a given scenario.