Top 8 Data Structures for Coding Interviews

Summary

In this video, the presenter walks through the top eight essential data structures needed for coding interviews. Beginning with simpler structures like arrays and linked lists, the discussion moves on to hash maps, queues, binary trees, and tries. The video explains the advantages, trade-offs, and time complexities of each data structure, from their memory organization to how they handle insertions, deletions, and searches. The video also covers more complex structures like heaps and graphs, providing viewers with valuable insights to tackle coding challenges efficiently.

Takeaways

• 😀 Arrays are stored contiguously in memory, allowing efficient access and insertion at the end, but can be slow for inserting or deleting elements in the middle.
• 😀 Linked lists store elements non-contiguously in memory, which allows efficient insertions and deletions in the middle but slower access times compared to arrays.
• 😀 Hash maps offer fast insertion, deletion, and searching in constant time (O(1)), but they do not maintain any specific order of elements.
• 😀 Queues are used for FIFO processing, with constant time operations for adding and removing elements from both ends. Double-ended queues allow insertion and removal from either side.
• 😀 Binary Search Trees (BSTs) maintain an ordered structure and allow efficient searching, insertion, and deletion in logarithmic time (O(log n)), provided the tree is balanced.
• 😀 Tries (Prefix Trees) are useful for storing strings and enable fast searching and insertion based on prefixes, commonly used in applications like autocomplete.
• 😀 Heaps, either min-heaps or max-heaps, provide constant-time access to the minimum or maximum value and logarithmic time insertion and deletion. They are useful for priority queues.
• 😀 Graphs are complex structures that represent relationships between nodes, and can be directed or undirected. They are typically represented by adjacency lists or matrices.
• 😀 When inserting or deleting from arrays in the middle, all subsequent elements may need to be shifted, resulting in O(n) time complexity.
• 😀 Each data structure comes with trade-offs in terms of efficiency for different operations, making it crucial to choose the right one based on the task at hand.

Q & A

What is the main benefit of arrays in terms of data access?

The main benefit of arrays is that they allow for constant-time (O(1)) access to any element, provided you know the index of the element.

What are the time complexities for inserting and removing elements at the end of an array?

Inserting or removing elements at the end of an array can be done in constant time, O(1).

Why do insertions or deletions in the middle of an array take O(n) time?

Inserting or deleting in the middle of an array takes O(n) time because all the elements after the insertion or removal point must be shifted to maintain the array's contiguous structure.

How are elements stored in a linked list compared to an array?

In a linked list, elements are not stored contiguously in memory. Each element (node) contains a value and a pointer to the next element in the list.

What is the advantage of using a linked list for inserting or removing elements?

A linked list allows for constant-time (O(1)) insertions and deletions at the ends or in the middle, since elements do not need to be shifted as in an array.

How do hash maps differ from arrays in terms of key-value pairs?

In hash maps, key-value pairs are stored, where the key can be any data type, not just an index like in arrays. Hash maps allow for efficient lookups, insertions, and deletions in O(1) time.

What are the limitations of hash maps compared to arrays?

Hash maps are unordered, meaning there is no concept of element order. This can be a limitation if order matters, unlike arrays where elements are stored in a specific order.

What is the difference between a queue and a double-ended queue (deque)?

A queue follows the First In, First Out (FIFO) principle, where elements are added at the back and removed from the front. A double-ended queue (deque) allows adding and removing elements from both ends, offering more flexibility.

Why are binary search trees (BST) efficient for searching and inserting data?

Binary search trees are efficient because they maintain a structure where left children are smaller and right children are larger than their parent, allowing for searches, insertions, and deletions in O(log n) time, assuming the tree is balanced.

What is the primary use case for tries (prefix trees)?

Tries are primarily used for storing strings and efficiently searching for words with common prefixes, such as in autocomplete systems. Insertions and searches take O(n) time, where n is the length of the word.

What are the advantages of heaps over other tree structures like binary search trees?

Heaps are specialized trees that allow for efficient retrieval of the minimum (min-heap) or maximum (max-heap) value in O(1) time, while insertions and deletions can be performed in O(log n) time. This makes heaps suitable for priority queue operations.

What is the role of an adjacency list in representing graphs?

An adjacency list is used to represent graphs by storing each node's neighbors in a list. This is an efficient way to manage graph connections and is especially useful for sparse graphs.