A Beginners Guide to Algorithmic Design and Data Structures

 

In the realm of computer programming, there exists a vast spectrum of algorithmic and data structuring techniques.  These algorithms and data structuring techniques are the backbone of software development and enable developers to create logical programming solutions capable of solving an array of computational challenges. With these profound technological innovations, the ability to effectively manipulate and organize data as well as optimize performance and operational efficiency has aided programmers to meet the demands of today’s technological landscape.

As a beginner new to the landscape of computer programming, it is important to note that each of these algorithms and data structuring techniques serve a specific purpose in the facilitation of problem solving and data management. Some common algorithms include search, sort, and numerical algorithms. Below is a brief explanation of the purpose of each as well variations of these algorithm types.

Searching Algorithms:  Designed to find specific elements or properties within a collection of elements. Some variations include linear, binary, and hashing.

Sorting Algorithms: Designed to arrange elements in a specific order. Some variations include bubble, merge, selection, insertion, quick, and heap.

Numerical Algorithms: Deal with numerical computations such as, linear equations, roots, integration, and differentiation.

In regard to common data structuring techniques, arrays, linked lists, trees, stacks and queues are some of the most commonly use. Provided below is a brief explanation of the purpose  of each.

Arrays: A collection of items of the same data type stored sequentially in memory allowing for random access.

Linked Lists: A linear data structure consisting of a series of connected nodes where each node stores the data and address of the next node.

Trees: A hierarchal data structure used in searching and sorting composed of nodes connected by edges with a single root node and child nodes branching outward.

Stacks and Queues: Abstract data types that follow Last-In-First-Out (LIFO) and First-In-First-Out principles, respectively.  Stacks are commonly used managing data in a sequential maaner where the last element added is the first to be removed. In queues, elements are added to the rear and removed from the front, thus ensuring that the first element added to the que it the first to be removed.

 When asking the question, “Are some algorithms and data structure designs better than others?”  the simple answer is yes. Each algorithm is intended to facilitate a specific action, and some are indeed better suited for certain tasks. Of course this is relative, and based on the functionality and desired output of the program however, when the right algorithm or data structure is chosen over another, it can significantly impact the performance, operation efficiency, and scalability of a program.