All data structures can be divided into two broad categories:

- Linear Data Structures
- Non-linear Data Structures

In these types of data structures, all the components are organized sequentially, one after another. Since all the elements are arranged in a specific order, implementing them is much easier. However, linear data structures might not be a good choice for complex programs, as they might not be able to tackle the operational complexities.

**Some popular linear data structures are - **

**Stack Data Structures**

The elements in Stack Data Structures are stored per the LIFO (Last In, First Out) principle. Let us imagine a stack of plates. It is only natural that the last plate is on the top of the pile and will be removed first. This data structure works in a similar method where the last element stored in the stack gets removed first.

**Linked Data Structures**

In this data structure model, the data components are connected through a series of nodes. Each node comprises the elements and the address to the adjacent node.

**Array Data Structures**

The data elements are stored in a serial memory where each array contains the same type of data elements. The programming language determines the data elements to be stored in the form of arrays.

**Queue Data Structures**

The Queue Data Structures operate just the opposite way of Stack Data Structures. It uses the FIFO (First In, First Out) principle, where the first data component stored will get removed first.

Unlike Linear Data Structures, the data elements in Non-linear Data Structures are not arranged in any sequential order. Instead, the data elements are stored in multilevel, and multiple runs are required to traverse through all of them completely. Each data element can have multiple paths to travel to another data element.

**The main Non-linear Data Structures are:**

**Graph Data Structures**

This data structure has a finite number of edges and vertices, the former used to connect with the latter. The data elements are stored in the vertices, and edges represent how the vertices are related to each other. The Graph Data Structure represents the network system and its principles in computer networks.

**Tree Data Structures**

This data structure uses a collection of data elements known as nodes linked together to simulate hierarchy. Instead of storing the data elements sequentially, they are stored on multiple levels in a hierarchical structure. The topmost node is called the root node.

Data structures and algorithms are essential tools for every programmer. While there are many such data structures, some data structure programs are necessary for all programmers to know. All potential programmers looking to enter the field of data analysis need to be familiar with and learn the basic data structures. While you are free to come up with your own data structures running various experiments, you need to learn the data structures mentioned below:

**Linear Data Structures: Linked, Arrays, Queues, and Stacks****Non-linear Data Structures: Trees and Graphs****Hash Table Data Structures**

In this data structure, each data element is associated with a key before storing them. This makes it an efficient and easy way to insert and search data irrespective of the size. The method also makes it easy for you to identify a specific data set from a group of similar ones. The hash table uses a hash function to map any sized data set to a fixed size - this is called the hash table, and the values that the function returns are called hash values. This data structure is most commonly used to construct associative arrays, database indexes, and sets.

**Heaps Data Structures**

Well, there is no easy way or shortcut while learning about data structures and algorithms. As a programmer, it is absolutely essential that you make yourself familiar with the above-mentioned data structures. There is no need for you to write your own data structures since most are provided by every major Software Developing Kit (SDK), such as C++Standard Temple Library (STL) or Java Development Kit (JDK). However, a comprehensive understanding of data structures and algorithms is required to know where to use them effectively.