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Data Structures using Java 🏆

Data structure interview questions with code in JAVA

Dynamic Programming

  1. Coin change problem 💰 (Number of ways sum can be achieved): You are given unlimited supply of coins of some denominations. Find the number of ways you can achieve in finding a sum value using these coins.

  2. Coin change problem (Minimum number of coins): You are given unlimited supply of coins of some denominations. Find the number of ways you can achieve in finding a sum value using these coins.

  3. Sieve of Eratosthenes (Find the prime numbers): Given a number n, print all primes smaller than or equal to n.

  4. Longest Common Subsequence: Given two sequences, find the length of the longest subsequence present in both of them.

Arrays

  1. Next greater element (Using stacks): Find the next greater element in an array. If not found for an element show null.

  2. Minimum element in array: Find the minimum element in an array.

  3. Leader in array: Find all the leaders in the array.

  4. KMP Algorithm: Knuth-Morris-Pratt string searching algorithm searches for occurrences of a word W within a main text string S.

Linked List

  1. Binary to Doubly linked list: Construction of doubly linked list from a binary tree.

Trees 🌳

  1. Binary search tree: construct: Construction of binary search tree from integer array.

  2. Pre-Order Traversal of BST: (Recursive): Pre-order traversal of Binary Tree recursively.

  3. Pre-Order Traversal of BST: (Non-Recursive): Pre-order traversal of Binary Tree non-recursively using stacks.

  4. In-Order Traversal of BST: (Recursive): In-order traversal of Binary Tree recursively.

  5. In-Order Traversal of BST: (Non-Recursive): In-order traversal of Binary Tree non-recursively using stacks.

  6. Post-Order Traversal of BST: (Recursive): Post-order traversal of Binary Tree recursively.

  7. Post-Order Traversal of BST: (Non-Recursive): Post-order traversal of Binary Tree non-recursively using stacks.

  8. Diameter of Binary Tree: Diameter of tree is the length of the longest path between any two nodes in a tree. The path may or may not pass through root.

  9. Deepest node in Binary Tree: Given a binary tree, find the deepest node in it.

  10. Boundary Traversal of Binary tree: Given a complete binary tree, traverse it such that all the boundary nodes are visited in Anti-Clockwise order starting from the root.

  11. Height of Binary Tree: Find the Maximum Depth or Height of a Tree.

  12. Level order traversing: Level order traversing of binary tree.

  13. Number of leaves of Binary tree: Print all the leaves of binary tree.

  14. Print all ancestors of node: Print all the ancestors of the node in a binary tree.

  15. Print all the paths from root to leaves: Print all the paths from root to leaf in a binary tree.

  16. Size of binary tree(Recursively): Find the size of the binary tree, recursively.

  17. Size of binary tree(Non-recursively): Find the size of the binary tree, non-recursively.

  18. Vertical sum of binary tree: Find all the vertical sums of the nodes.

  19. Lowest common ancestor of two nodes: Given a binary tree (not a binary search tree) and two values say n1 and n2, write a program to find the least common ancestor.

  20. Diagonal sum of two nodes: Print the sum of every diagonal in binary tree.

  21. Boundary traversal of binary tree: Print the boundary traversal of binary tree.

  22. Top view of binary tree: Print the top view of binary tree.

  23. Bottom view of binary tree: Print the bottom view of binary tree.

  24. Side view of binary tree(Left and Right view): Print the side views of binary tree both the left side view and right side view.

  25. Inorder predecessor of binary tree: Print the inorder predecessor of a node in a binary tree.

  26. Inorder successor of binary tree: Print the inorder successor of a node in a binary tree.

Graphs

  1. BFS: Breadth First Search (BFS) algorithm traverses a graph in a breadth ward motion and uses a queue to remember to get the next vertex to start a search.

  2. DFS: Depth First Search (DFS) algorithm traverses a graph in a depth ward motion and uses a stack to remember to get the next vertex to start a search.

  3. Dijkstra Algorithm: an algorithm for finding the shortest path from a starting node to a target node in a weighted graph.

  4. Bellman Ford Algorithm: an algorithm that computes the shortest paths from a single source vertex to all the other vertices in a weighted digraph.

  5. Floyd Warshell Algorithm: an algorithm for finding the shortest path between all the pairs of vertices in a weighted graph. This algorithm works for both the directed and undirected weighted graphs.

  6. Disjoint sets: A disjoint-set data structure is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. This can be used for determining if two elements are in the same subset. Union: Join two subsets into a single subset.

  7. Find cycle in undirected graph(disjoint sets): Finding the cycle inside an undirected graph using the disjoint sets.

  8. Find cycle in directed graph(dfs): Finding the cycle inside an directed graph using the dfs.