# Types of Trees in Data Structure Explained Including Properties and Applications

In the context of data science, the term “tree” means the hierarchical structure of available data. The nonlinear framework consists of nodes linked by edges. Several sets of data structures operate upon a linear structure of data.

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### Why Is It Necessary to Define Trees in Data Structure?

There is a notable problem with the data structures that are linear. Here, when there is an increase in the size of the data, the complexity of the whole data structure rises simultaneously. In contrast, tree data structure allows a faster approach to the collected data because of its nonlinear structure.

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### Types of Trees in Data Structure

While training in data science, you will learn about four types of trees in the data structure. They are broken into categories in terms of nodes, linearity, and variation. Let us understand the four types of nodes that you will study in the best courses for data analytics.

• #### Binary Tree

A binary tree has structural composition where every parent node available in the data set has two different child nodes. A child node is a descendant of a node and carries similar properties. Note that a node in this tree can either stay separate and not have any nodes or have two nodes in total.

The properties of a binary tree are as follows.

• The highest number of nodes that this tree can have at any level of categorization is two.
• In total, the tree can only have four levels. Therefore, the maximum nodes possible here are 14, all belonging to one parent node.
• The minimum nodes at height h that can practically exist = h + 1.
• The maximum nodes at height h that can practically exist = (20 + 21 + 22 + … 2h = 2h + 1)

The binary tree can further be subdivided into different versions, such as,

• Full binary tree
• Perfect binary tree
• Complete binary tree
• Pathological or degenerate tree
• Skewed tree
• Balanced tree

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• #### Binary Search Tree

A binary search tree often defines itself as nonlinear.

In this tree structure, a single node breaks into several nodes that spread apart like branches. Every node on this nonlinear tree can link to a maximum of two child nodes only. It gets the name of the binary search tree because the parent node here can have only two child nodes.

In the binary search tree, users can search through entire nodes for a single element in 0(log(n)) processing time.

• #### AVL Tree

One of the types of variants for a binary tree is the AVL tree. It is a combination of the binary search tree and binary tree types. Therefore, it reflects the properties of both types of trees.

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The AVL tree has a self-balancing nature in general. So, it is crucial to balance both the subtrees here carefully. To simplify, the number of nodes in both tree branches should stay the same.

For accurate measurement, use the balancing factor (difference in the number of nodes between the left and right subtree). Here, the ideal value is between either 0, 1, or -1.

• #### B-tree

A subsidiary of the binary search tree, B-tree has a more generalized structure. Another name for this is a height-balanced m-way tree. The “m” highlights the hierarchy of the data tree.

Every node can have child nodes that are more than two. There can also be more than one key in a B-tree. Leaf nodes must hold an equivalent level of hierarchy. Also, the various keys stay in place according to the hierarchy of all nodes.

#### Salary Potential in Data Analytics Career Field

As a data analyst, monitoring, assessing, and interpreting the data structure is an essential function. You can expect high salary potential in this career, based on your skill and experience level. In India, the following salary structures are common.

• Less than one year experience: ₹342,291/year
• One to four years of experience: ₹426,402/year
• Five to nine years of experience: ₹696,043/year
• 10 to 19 years of experience: ₹934,951/year
• 20+ years of experience: ₹1,750,000/year

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Note. These are approximate figures.

#### Conclusion

Four types of trees play significant functions in running different operations and processes in a data structure. You can conduct a better-quality assessment of data with this knowledge and create insightful solutions after that.

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