K9: algorithms, logic and data structures relevant to software development for example:- arrays- stacks- queues- linked lists- trees- graphs- hash tables- sorting algorithms- searching algorithms- critical sections and race conditions
| Assessment Method | Pass | Distinction |
|---|---|---|
| Work-based project | Outlines and applies the rationale and use of algorithms, logic and data structures. (K9, S16) | NA |
When an apprentice software developer is asked to demonstrate their knowledge of algorithms, logic, and data structures relevant to software development, it means they are expected to have a solid understanding of the following concepts:
Arrays: An array is a collection of elements of the same type stored in contiguous memory locations. It allows efficient access to elements using indices.
Stacks: A stack is a data structure that follows the Last-In-First-Out (LIFO) principle. It supports two basic operations: push (adds an element to the top) and pop (removes the top element).
Queues: A queue is a data structure that follows the First-In-First-Out (FIFO) principle. It supports two basic operations: enqueue (adds an element to the rear) and dequeue (removes the front element).
Linked Lists: A linked list is a data structure composed of nodes, where each node contains data and a reference (or link) to the next node. It provides dynamic memory allocation and efficient insertion and deletion operations.
Trees: A tree is a hierarchical data structure consisting of nodes connected by edges. It has a root node and can have child nodes. Examples include binary trees, binary search trees, and AVL trees.
Graphs: A graph is a collection of nodes (vertices) connected by edges. It is a versatile data structure used to represent relationships between objects. Graphs can be directed or undirected.
Hash Tables: A hash table (or hash map) is a data structure that uses a hash function to map keys to values. It provides efficient insertion, deletion, and retrieval operations by reducing the search space.
Sorting Algorithms: Sorting algorithms arrange elements in a particular order (ascending or descending). Examples include bubble sort, selection sort, insertion sort, merge sort, quicksort, and heapsort.
Searching Algorithms: Searching algorithms help locate an element within a data structure. Examples include linear search, binary search (for sorted arrays), and depth-first search (for graphs and trees).
Critical Sections and Race Conditions: Critical sections refer to parts of code that must be executed exclusively to avoid data inconsistency. Race conditions occur when multiple threads or processes access shared resources simultaneously, leading to unexpected behavior.
Having knowledge of these concepts demonstrates a strong foundation in fundamental data structures, algorithms, and problem-solving skills, which are essential for software development.