You'll find that using data structures is a pretty common occurrence as a programmer, so being proficient with basic data structures like binary trees, stacks, and queues is vital to your success. But if you want to improve your skills and stand out from the crowd, you’re going to want to get familiar with advanced data structures as well.

Advanced data structures are an essential component of data science, and they help clear up inefficient management and provide storage for large sets of data. Software engineers and data scientists constantly make use of advanced data structures to design algorithms and software.

Read on as we discuss the advanced data structures every expert programmer should know about.

1. Fibonacci Heap

If you’ve put some time into learning data structures already, you must be familiar with binary heaps. Fibonacci heaps are pretty similar, and they follow the min-heap or max-heap properties. You can think of a Fibonacci heap as a collection of trees where the minimum or maximum value node is always at the root.

fibonacci heap
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The heap also fulfills the Fibonacci property such that a node n will have at least F(n+2) nodes. Within a Fibonacci heap, the roots of each node are linked together, usually through a circular doubly-linked list. This makes it possible to delete a node and concatenate two lists in just O(1) time.

Fibonacci heaps are much more flexible than binary and binomial heaps, making them useful in a wide range of applications. They're commonly used as priority queues in Dijkstra's algorithm to improve the algorithm's running time significantly.

2. AVL Tree

avl trees
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AVL (Adelson-Velsky and Landis) trees are one of the many different types of trees in computer science. They are essentially a self-balancing binary search tree. Standard Binary Search Trees can get skewed in certain edge cases and have a worst-case time complexity of O(n), making them inefficient for real-world applications. Self-balancing trees automatically change their structure when it violates the balancing property.

In an AVL tree, each node contains an extra attribute that contains its balancing factor. The balance factor is the value obtained by subtracting the height of the left subtree from the right subtree at that node. The self-balancing property of the AVL tree requires the balance factor always to be -1, 0, or 1.

If the self-balancing property (balance factor) is violated, the AVL tree rotates its nodes to preserve the balance factor. An AVL tree uses four main rotations—right rotate, left rotate, left-right rotate, and right-left rotate.

The self-balancing property of an AVL tree improves its worst-case time complexity to just O(log n), which is significantly more efficient compared to the performance of a Binary Search Tree.

Since the AVL tree maintains itself via a balance factor, you can calculate the minimum number of nodes, given the height. The recurrence relation comes down to N(h) = N(h-1) + N(h-2) + 1 and there must be at least three nodes in the AVL tree (n > 2). The base cases of the recurrence relation are N(0) = 1 and N(1) = 2 respectively.

3. Red-Black Tree

red black trees
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A Red-Black tree is another self-balancing binary search tree that uses an extra bit as its self-balancing property. The bit is usually referred to as red or black, hence the name Red-Black tree.

Each node in a Red-Black is either red or black, but the root node must always be black. There cannot be two adjacent red nodes, and all leaf nodes must be black. These rules preserve the self-balancing property of the tree.

In contrast to Binary Search trees, Red-Black trees have approximately O(log n) efficiency, making them far more efficient. However, AVL trees are much more balanced due to a definitive self-balancing property.

Improve Your Data Structures

Knowing advanced data structures can make a big difference in job interviews and could be the factor that separates you from the competition. Other advanced data structures that you should look into include n-Trees, Graphs, and Disjoint Sets.

Identifying an ideal data structure for a given scenario is part of what makes a good programmer great.