This paper provides an overview of the fundamental concepts typically found in a first module of , covering the basic building blocks of software efficiency and organization. Algorithms and Data Structures: Fundamental Foundations 1. Introduction
Before implementing structures, one must understand how to measure them. (Big O) allows programmers to predict how the execution time or memory usage of an algorithm grows as the input size ( ) increases. : Constant time (e.g., accessing an array index). : Linear time (e.g., searching an unsorted list). : Quadratic time (e.g., nested loops in simple sorting). 3. Linear Data Structures Algoritmos y Estructuras de Datos.part1.rar
Arrays are collections of elements stored in contiguous memory locations. Fast access via index ( Cons: Fixed size; insertions and deletions are costly ( ) as elements must be shifted. 3.2 Dynamic Structures: Linked Lists This paper provides an overview of the fundamental
Part 1 of this study focuses on structures where elements are arranged sequentially: 3.1 Static Structures: Arrays (Big O) allows programmers to predict how the
Simple algorithms like Bubble Sort or Insertion Sort provide a conceptual base for more complex divide-and-conquer methods. 6. Conclusion