Introduction to Data Structures and Algorithms

Vocabulary flashcards covering fundamental concepts, classifications, properties, and complexity measures in Data Structures and Algorithms.

Data Structure

A way of collecting and organising data to perform operations effectively and efficiently on them while utilizing minimum memory space and execution time.

Linear Data Structure

A data structure where elements are arranged in one dimension (also known as a linear dimension); examples include lists, stack, and queue.

Non-Linear Data Structure

A data structure where elements are arranged in one-many, many-one, and many-many dimensions; examples include tree, graph, and table.

Data Structures Classification

The hierarchical organization of data structures into Linear structures (Static: Array; Dynamic: List, Stack, Queue) and Non-Linear structures (Graph, Tree).

Algorithm

A finite set of instructions or logic, written in order, to accomplish a certain predefined task, representing the core problem logic rather than complete code.

Input (Algorithm Property)

The property stating that there should be $0$ or more inputs supplied externally to the algorithm.

Output (Algorithm Property)

The property stating that there should be at least $1$ output obtained from the algorithm.

Definiteness

An algorithm property requiring that every step of the algorithm should be clear and well defined.

Finiteness

An algorithm property requiring that the algorithm should have a finite number of steps.

Correctness

An algorithm property requiring that every step of the algorithm must generate a correct output.

Time Complexity

A way to represent the amount of time required by the program to run till its completion, counted for all statements and primarily determined by input data size.

Space Complexity

The amount of memory space required by an algorithm during the course of its execution, counted for all variables, inputs, and outputs, and primarily determined by auxiliary variable size.