The Core of Programming - Data Structures and Algorithms

Even for programs performing the same task, their efficiency can vary greatly.

An efficient program uses less memory and executes quickly, whereas an inefficient one uses a lot of memory and has a slow execution speed.

The efficiency of a program depends on the Data Structures and Algorithms.

Data Structures define ways to organize and structure data in a computer to store, manage, and process it efficiently.

In simpler terms, a data structure is how you define storing, accessing, and modifying data.

Representative data structures include arrays, stacks, queues, linked lists, hash tables, and graphs.

• Array: A data structure that stores elements of the same data type in sequence. Python’s list has a structure similar to an array.

• Stack: Similar to stacking plates, it follows the Last In, First Out (LIFO) principle where items added last are removed first. Additions and removals occur only at one end. The back button in web browsers is a practical example of stack behavior.

• Queue: Operates on the First In, First Out (FIFO) principle like a queue line, where data is added at one end and removed from the other. A print queue is an example utilizing queue behavior.

• Linked List: A sequence of nodes where each node contains data and a reference (address) to the next node, allowing flexibility in data insertion and deletion. A playlist like "Song A → Song B → Song C" can use linked lists.

• Hash Tables: Designed to store data in key-value pairs for fast retrieval. Python’s dictionary provides functionality similar to hash tables.

• Graph: Composed of vertices (nodes) and edges connecting them, representing complex relationships between elements. For instance, a social network mapping friend connections or a city road network can be depicted as a graph.

An Algorithm is a set of step-by-step instructions and procedures to solve a specific problem.

In programming, algorithms are used to process data, perform calculations, and make automated decisions.

Examples of algorithm applications include:

• Sorting Algorithms: Arrange data in a specific order quickly and efficiently.

• Search Algorithms: Efficiently locate a desired item within a data set.

• Computational Algorithms: Perform mathematical calculations such as finding the greatest common divisor, identifying prime numbers, or generating the Fibonacci sequence.

• Graph Algorithms: Use graph data structures to find the shortest paths or optimize network flow.