10 Most Important Data Structures For Coding Interviews

Here are 10 of the most important data structures you should be familiar with for coding interviews:

1. Arrays: A fundamental data structure that stores a fixed-size collection of elements all of the same data type. Arrays are efficient for random access (accessing any element by its index) but can be slow for insertions and deletions in the middle.
2. Linked Lists: A linear data structure where elements are not stored contiguously in memory. Each element (node) contains data and a reference (pointer) to the next node in the list. Linked lists are useful for insertions and deletions at any point but slower for random access compared to arrays.
3. Stacks: LIFO (Last In, First Out) data structure that operates like a stack of plates. You can only add or remove elements from the top. Stacks are commonly used for function calls, implementing undo/redo functionality, and expression evaluation.
4. Queues: FIFO (First In, First Out) data structure that functions like a queue or waiting line. You add elements to the back (enqueue) and remove them from the front (dequeue). Queues are useful for processing tasks in a specific order, managing buffers, and breadth-first search algorithms.
5. Hash Tables: Implement a key-value pair storage mechanism. A hash function is used to map a key to a specific location in the table. Hash tables offer fast average-case lookups, insertions, and deletions.
6. Trees: Hierarchical data structures that consist of nodes connected by edges. Each node can have child nodes, forming a parent-child relationship. Binary trees (each node has at most two children) are a common type, used for efficient searching and sorting (like binary search trees) and representing hierarchical relationships.
7. Graphs: Collection of nodes (vertices) connected by edges. Graphs model relationships between objects and are used for problems like finding shortest paths (navigation), network modeling, and social network analysis.
8. Tries: Specialized tree-like structure where data is stored using prefixes. Tries are efficient for searching sets of strings with a common prefix and used for autocomplete functionality and spell checking.
9. Heaps: Specialized tree-based structure where elements have a specific ordering property (like max-heap or min-heap). Heaps are efficient for priority queues, sorting algorithms (heap sort), and implementing Huffman coding.

While these are the core structures, some interviews might explore more advanced ones like:

10. Disjoint-Set Union (DSU): Used for maintaining disjoint sets (sets that don't intersect) and finding the representative element of a set efficiently. Useful for problems like finding connected components in a graph.

Remember, focus on understanding the core concepts, common operations (insertion, deletion, search, traversal), and time complexities associated with these data structures. Practice implementing them in your preferred programming language to solidify your understanding.

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