Data Structures and Algorithms#
Data Structures and Algorithms (DSA) represents the foundational building blocks of computer science that enable efficient data organization and problem solving.
Data structures and algorithms go hand in hand. A data structure is not worth much if you cannot search through it or manipulate it efficiently using algorithms, and the algorithms are not worth much without a data structure to work on.
An algorithm is a step-by-step set of rules or instructions used to solve a problem, perform a calculation, or complete a specific task.
A data structure is a specialized format for organizing, processing, retrieving, and storing data in a computer system so it can be used efficiently. It provides a physical implementation for abstract concepts, ensuring that code can access and modify information quickly and accurately.
Space complexity is a metric that quantifies the total memory space an algorithm or data structure requires to run to completion as a function of the input size (n). It is expressed using Big O notation to define how memory requirements scale rather than measuring exact bytes, which vary by hardware.
Time Complexity is a theoretical measure that quantifies the amount of time an algorithm takes to run as a function of the length of its input (n). Instead of measuring actual seconds—which change based on hardware, compilers, and processors—time complexity counts the number of elementary operations or code statements executed.