Data Structure & Algorithm

Trees in Data Structure Every Data Scientist Should Know About

What image pops in your mind when you hear the word – tree? An image of leaves, branches, and roots, right? 

You’d be surprised to learn that a tree in computer science has a similar structure to the conventional tree. The only catch here is that it is carved upside down. 

Data structures are a pattern to arrange the desired data on a computer disk to facilitate the convenient display and storage of that data.  A tree can be defined as a hierarchical data structure that is used to define the relationship between different nodes. 

Data scientist is thought to be a lucrative career choice due to continuous technological advancements. 

You’d be surprised to learn that within India and the US, tech-firms employed nearly 3,00,000 data analysts and 50,000 data scientists in the year 2021. 

Therefore, understanding the various types of trees in data structures & algorithms must be on your to-do list. 

In this article, we will briefly touch down upon the topics of properties and their trees, to give you a comprehensive gist about the subject. 

Let’s get started! 

What are the Basic Properties of Trees? 

Trees are a type of non-linear data structure wherein the data is organized hierarchically. The nodes in a tree are connected to each other by edges and the node consists of a value. Furthermore, all nodes in a tree can also have a child node of its own. 

There are three basic properties of a tree: 

  1. A tree can either contain zero nodes or it can be made of one special node known as the root node. The root node can further have zero or more sub trees. 
  2. Every edge in a tree structure originates either directly or indirectly from the root node. 
  3. Every child node in the tree structure can have only one parent node. However, a single parent node can have multiple child nodes. 

Now that you have an idea about what trees are and the three basic principles they follow, let’s find out more about the types of trees in data structures. 

Types of Trees in Data Structures 

In this section, we will briefly talk about the 10 types of trees structures and their uses: 

  • General Tree

A general tree in a data structure is the one where there are no limitations on the hierarchical structure. It can have either zero or multiple child nodes. The topmost node in a general tree is known as the root node. 

This type of tree data structure is used to store folder structures and other types of hierarchical data. 

  • Binary Tree 

Binary tree is a non-linear data structure wherein each parent node has a maximum of two child nodes. A typical binary tree consists of three components – a left reference, a right reference, and a data element. It has further five types – full, complete , perfect , balanced and degenerate binary tree. 

A binary tree has multiple applications such as – data compression, hashing, binary search trees, build syntax trees, implement expression solvers and expression parsers, and store router-tables. 

  • Binary Search Tree 

The binary search tree is also a node-based data structure and it has the following basic properties: 

  • The left subtree in a binary search tree node is made up of nodes with lesser keys when compared to the node’s key. (Value of left subtree node must be lesser than the parent node)
  • The right subtree is made up of nodes with higher keys when compared to the node’s key. (Value of right subtree node must be higher than the parent node)
  • It is important that both the right and left subtree be a binary search tree. 

Binary search trees are used to initiate sorting algorithms, can be utilized as priority queues, and are of prime importance in applications where the data is either constantly entering or leaving. 

  • AVL Tree

The AVL tree has been named after the scientists – Adelson, Velski, and Landis. They are regarded as a self-balancing type of binary search tree. In a balanced AVL tree, the heights of the child nodes of any parent node differ by a maximum of one. Rebalancing is done if the difference between the heights of child subtrees is more than one. 

AVL trees are popularly used where frequent insertions in data are seen and several types of memory management subsystems as well.  

  • Red Black Tree 

Red black trees are also a self-balancing tree just like the AVL tree. However, the difference between the two is that red black trees do not require more than 2 rotations to balance themselves. They consist of an extra bit to define the colour (red or black) of a node. This ensures that the tree remains balanced during multiple insertions and deletions. 

Red black trees are used by data scientists in computational geometry, completely fair scheduler in Linux kernels, etc. 

  • Splay Tree 

Splay tree is another type of binary search tree that can perform rotational operations to manipulate and adjust a code. The node that was accessed by you recently is rotated and arranged as a root node. Although this is not a height-balanced tree, it is still considered a balanced one. 

A splay tree is used to compress data, implement caches, and in garbage collectors. 

  • Treap  

Treats are regarded as a combination of heaps and trees in data structures. Due to this combination, treaps hold a priority just like heaps and a key just like the binary search trees. Therefore, the keys in treap follow the properties of a binary search tree and the priorities follow the properties of heap. 

They are used to perform a particular set of operations faster and are of key importance in public-key crypto systems. 

  • B-Tree 

This is also a self-balancing tree that allows deletions, sequential access, insertions, and search in a logarithmic time. In a B-tree, a parent node can have more than two child nodes. Such trees are compatible with file systems and databases that are responsible for reading and writing large data blocks. 

B-trees are used in the indexing of databases to amp up the search. They’re also used in file systems for directories implementation. 


If you want to write a clean code during your interview, then you must study in-depth about the types of trees during your aptitude preparation

Knowledge of trees can certainly help you secure a lucrative job as a data scientist in top IT firms. 

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