Dynamic Programming - DP
Optimizes problem-solving through subproblem breakdown, storing solutions to enhance efficiency.
Optimizes problem-solving through subproblem breakdown, storing solutions to enhance efficiency.
Strings are sequences of characters, fundamental in programming for text representation, manipulation, and processing.
A trie is a tree-like data structure for storing and retrieving dynamic sets, often used for efficient string matching.
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle for storing and retrieving elements.
Recursion is a programming technique where a function calls itself to solve smaller instances of a problem, aiding in iteration.
A queue is a linear data structure that follows the First In, First Out (FIFO) principle for storing and retrieving elements.
A linked list is a linear data structure where elements are connected via pointers, allowing dynamic memory allocation and efficient insertions.
A graph is a versatile data structure consisting of nodes and edges, representing relationships between entities in a network or system.
List of top data structure and algorithmic problems commonly asked in technical interviews.
Binary Tree: Tree data structure where each node has at most two children, left and right, facilitating efficient search and traversal.
Binary Search Tree: Binary tree with the property that for each node, values in its left subtree are less, and in the right, greater.
Arrays: Ordered, fixed-size data structures storing elements of the same type, accessed by index, crucial for efficient data manipulation.
Master bitwise operators (AND, OR, XOR, NOT) to solve complex algorithmic problems with high performance and low memory.
Python: Readable, versatile, high-level language with extensive libraries for rapid development across various domains.
Java: Object-oriented, versatile language for diverse application development with simplicity, reliability, and extensive libraries.