" ... a data structure is a data organization, management, and storage format that is usually chosen for efficient access to data."
The study of organizing and storing data for efficient access and manipulation. It includes algorithms for sorting, searching, and graph traversal, as well as data structures like arrays, linked lists, and trees.
Basic Data Structures: Understanding fundamental data structures such as arrays, linked lists, stacks, queues, and trees. Knowing how to manipulate data using these structures.
Algorithms: Basic algorithms such as sorting, searching, and traversing. Knowing how to implement them and analyzing their complexity for performance.
Recursion: Understanding what is recursion, how it works, and its applications in programming.
Dynamic Programming: Knowing how to break down complex problems into smaller subproblems and using dynamic programming to solve them efficiently.
Graph Algorithms: Understanding graph data structures, graph algorithms like shortest path algorithms, spanning tree algorithms, and topological sorting.
String Algorithms: Understanding string data structures and algorithms like generating permutations, finding substrings, and detecting palindromes.
Advanced Data Structures: Understanding advanced data structures like hash maps, binary heaps, and AVL trees.
Tree Algorithms: Understanding tree data structures, tree traversal algorithms, and tree balancing algorithms.
Computational Geometry: Algorithms to solve problems involving geometric data such as distance between points, finding intersections, and convex hulls.
Dynamic Data Structures: Advanced data structures that are mutable such as binary search trees, hash maps, and heaps.
Bit Manipulation: Understanding how to use bitwise operators, and manipulating binary data.
Memory Management: Understanding dynamic memory allocation and dynamic data structures.
Divide and Conquer: A problem-solving method that involves breaking down problems into smaller subproblems and solving them recursively.
Hashing: Understanding hash functions and how to use them to store and retrieve data efficiently.
Greedy Algorithms: A class of algorithms that always makes the locally optimal choice in hopes of finding a global optimum.
Backtracking: A problem-solving method that involves exploring all possible solutions, and rejecting the ones that fail to satisfy or solve the problem.
Heuristic Algorithms: Approximation algorithms that solve problems by short-cutting or relaxing problem criteria.
Brute Force Algorithms: Exhaustive search algorithms that systematically generate every possible solution and select the optimal one.
Parallel Algorithms: Algorithms designed for execution on parallel or multicore systems.
Complexity Theory: The study of how much time and space a problem requires to solve given a certain input size.
Arrays: A fixed size collection of elements of a particular data type.
Linked Lists: A collection of nodes, where each node contains a reference to the next node in the list.
Stacks: A collection of elements that allows access to only the last added element.
Queues: A collection of elements where the first element added is the first one to be removed.
Trees: A collection of nodes where each node has a parent and zero or more child nodes.
Graphs: A collection of nodes and edges that connect them.
Hash tables: A data structure that maps keys to values using a hash function.
Heaps: A special type of binary tree that satisfies the heap property.
Sorting algorithms: Algorithms used to arrange the elements in a data structure in a specific order, such as ascending or descending.
Searching algorithms: Algorithms used to find a specific element in a data structure.
Tree traversal algorithms: Algorithms used to traverse the nodes of a tree in a specific order, such as inorder, preorder, or postorder.
Graph traversal algorithms: Algorithms used to traverse the nodes and edges of a graph, such as breadth-first search or depth-first search.
Dynamic programming algorithms: Algorithms used to solve complex problems by breaking them down into smaller subproblems and storing the solutions to those subproblems.
Divide and conquer algorithms: Algorithms used to solve problems by breaking them down into smaller subproblems and solving those subproblems recursively.
Greedy algorithms: Algorithms that make the locally optimal choice at each step to find a globally optimal solution.
Backtracking algorithms: Algorithms used to find all possible solutions to a problem by searching through all possible paths.
String matching algorithms: Algorithms used to find a specific pattern within a string of text, such as the Knuth-Morris-Pratt algorithm or the Boyer-Moore algorithm.
"... a collection of data values, the relationships among them, and the functions or operations that can be applied to the data."
"... it is an algebraic structure about data."
"... efficient access to data."
"A data structure is a data organization ... chosen for efficient access to data."
"A data structure ... is usually chosen for efficient access to data."
"A data structure is a data organization, management, and storage format..."
"... a data structure is a collection of data values, the relationships among them..."
"... the functions or operations that can be applied to the data..."
"... a data structure is a data organization, management, and storage format..."
"... a data structure is usually chosen for efficient access to data."
"... a data structure is usually chosen for efficient access to data."
"Computer science."
"A data structure is a data organization, management, and storage format that is usually chosen for efficient access to data."
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"... a collection of data values, the relationships among them, and the functions or operations that can be applied to the data..."
"A data structure is a data organization, management, and storage format..."
(N/A - no direct quote in the given paragraph)
"A data structure is usually chosen for efficient access to data."
(N/A - no direct quote in the given paragraph)