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kdn251 Interview Preparation Resources

repository·master·Indexed 13 days ago

https://github.com/kdn251/interviews

A curated collection of resources for software engineering technical interviews, featuring Java implementations of algorithmic problems, data structure complexities (Linked Lists, Stacks, Queues, Trees, Heaps, Graphs), sorting and graph algorithm references, bitmasking operations, and a list of online judges and mock interview platforms.

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What's inside kdn251 interviews

  1. Access interview preparation resources

    master

    This repository serves as a central hub for software engineering interview preparation. You can access various resources including:

  2. Explore the Interview Problem Repository Structure

    master

    The repository is organized by algorithmic patterns and data structures. Each directory contains Java implementations of common interview problems. Key categories include:

    • Array: Array-based problems (e.g., mergeIntervals.java, searchInRotatedSortedArray.java).
    • Backtracking: Problems like letterCombinationsOfAPhoneNumber.java.
    • BinarySearch: Search algorithms (e.g., sqrt(x).java).
    • BitManipulation: Bitwise operations (e.g., hammingDistance.java).
    • BreadthFirstSearch / DepthFirstSearch: Graph and Tree traversals.
    • DynamicProgramming: Optimization problems (e.g., climbingStairs.java).
    • HashTable: Hash-based problems (e.g., twoSum.java).
    • LinkedList: Linked list manipulations.
    • Stack / Queue: Linear data structure problems.
    • Tree / Trie: Hierarchical data structures.
    • TwoPointers: Pointer-based array/string problems.
    .
├── Array
├── Backtracking
├── BinarySearch
├── BitManipulation
├── BreadthFirstSearch
├── DepthFirstSearch
├── Design
├── DivideAndConquer
├── DynamicProgramming
├── HashTable
├── LinkedList
├── Queue
├── Sort
├── Stack
├── String
├── Tree
├── Trie
└── TwoPointers

  3. Browse interview questions by topic

    master

    The repository organizes interview preparation materials into specific computer science topics. You can find Java implementations of common algorithmic problems categorized by their underlying data structures or algorithmic techniques.

    Available topics include:

    • Array: e.g., maximumSubarray.java, mergeIntervals.java
    • Backtracking: e.g., letterCombinationsOfAPhoneNumber.java
    • BinarySearch: e.g., sqrt(x).java
    • BitManipulation: e.g., hammingDistance.java
    • BreadthFirstSearch: e.g., cloneGraph.java
    • DepthFirstSearch: e.g., numberOfIslands.java
    • Design: e.g., zigzagIterator.java
    • DivideAndConquer: e.g., kthLargestElementInAnArray.java
    • DynamicProgramming: e.g., climbingStairs.java
    • HashTable: e.g., twoSum.java
    • LinkedList: e.g., reverseLinkedList.java
    • Queue: e.g., movingAverageFromDataStream.java
    • Sort: e.g., meetingRooms.java
    • Stack: e.g., decodeString.java
    • String: e.g., longestPalindrome.java
    • Tree: e.g., invertBinaryTree.java
    • Trie: e.g., implementTrie.java
    • TwoPointers: e.g., 3Sum.java

  4. Trie (Prefix Tree) concepts

    master

    A Trie (or radix/prefix tree) is a search tree used to store a dynamic set or associative array where keys are usually Strings.

    Key Characteristics:

    • Nodes do not store the key itself; the key is defined by the node's position in the tree.
    • All descendants of a node share a common prefix.
    • The root is associated with an empty String.

  5. Heap concepts and complexity

    master

    A Heap is a specialized tree-based structure satisfying the heap property: if A is a parent of B, the key of A is ordered relative to the key of B consistently across the entire heap.

    Types:

    • Max Heap: Parent keys are $\ge$ children keys; the highest key is at the root.
    • Min Heap: Parent keys are $\le$ children keys; the lowest key is at the root.

    Time Complexity:

    • Access Max / Min: O(1)
    • Insert: O(log(n))
    • Remove Max / Min: O(log(n))

  6. Segment Tree concepts and complexity

    master

    A Segment Tree is a tree data structure used for storing intervals or segments. It allows for querying which stored segments contain a specific point.

    Time Complexity:

    • Range Query: O(log(n))
    • Update: O(log(n))

  7. Hashing and Collision Resolution

    master

    Hashing maps data of arbitrary size to a fixed size. A collision occurs if two keys map to the same hash value.

    Hash Map: A structure mapping keys to values using a hash function to find an index in an array of buckets.

    Collision Resolution Strategies:

    • Separate Chaining: Each bucket contains a list of entries for that index. Operations involve finding the bucket (constant time) and iterating through the list.
    • Open Addressing: When a collision occurs, the system examines subsequent buckets in a sequence until an unoccupied slot is found.

  8. Understand Algorithm Complexity Notations

    master

    The repository provides a conceptual overview of Big O notation and other asymptotic notations used to describe algorithm complexity:

    • Big O (O): Represents the upper bound of an algorithm, typically describing the worst-case scenario.
    • Little o (o): Describes a tight asymptotic upper bound.
    • Big Omega (Ω): Describes the asymptotic lower bound of an algorithm.
    • Little omega (ω): Describes a specific lower bound that is not necessarily tight.
    • Theta (Θ): Describes the tight bound (both upper and lower) of an algorithm.

  9. Graph concepts

    master

    A Graph is an ordered pair $G = (V, E)$ consisting of a set $V$ of vertices (nodes) and a set $E$ of edges (arcs) connecting them.

    Types:

    • Undirected Graph: The adjacency relation is symmetric (if $u \to v$ exists, then $v \to u$ also exists).
    • Directed Graph: The adjacency relation is not symmetric (an edge $u \to v$ does not imply $v \to u$).

  10. Queue concepts and complexity

    master

    A Queue is a collection of elements following the First In, First Out (FIFO) principle. The oldest added object is the first to be removed.

    Principal Operations:

    • enqueue: Inserts an element into the queue.
    • dequeue: Removes an element from the queue.

    Time Complexity:

    • Access: O(n)
    • Search: O(n)
    • Insert: O(1)
    • Remove: O(1)

  11. Binary Search Tree (BST) concepts and complexity

    master

    A Binary Search Tree (BST) is a binary tree where for every node:

    • The value in the node is $\ge$ any value in its left sub-tree.
    • The value in the node is $\le$ any value in its right sub-tree.

    Time Complexity:

    • Access: O(log(n))
    • Search: O(log(n))
    • Insert: O(log(n))
    • Remove: O(log(n))

  12. Understand Runtime Analysis Notations

    master

    Asymptotic notations used to describe algorithm complexity:

    • Big O Notation: Describes the upper bound (worst case scenario).
    • Little o Notation: Describes an upper bound that is not asymptotically tight.
    • Big $\Omega$ (Omega) Notation: Provides an asymptotic lower bound.
    • Little $\omega$ (omega) Notation: Provides a lower bound that is not asymptotically tight.
    • Theta $\Theta$ Notation: Provides a tight bound where the function is sandwiched between two constants for sufficiently large values.