What are the types of data structures a data analyst must know?

What are the types of data structures a data analyst must know?

Data structures are essential programming methods of storing, arranging, retrieving & modifying data. It is an efficient method of carrying out numerous data management procedures. Businesses employ different types of data structures to handle massive amounts of data to make informed decisions. Data structures have a vast and diversified range of applications, the reason why data structures are employed in all aspects of computer science, including artificial intelligence, graphics, operating systems, and so on. An appropriate selection of this data structure can enhance how well the computer program or algorithm may perform.

For specialized purposes, various types of data structures, both simple and complex, are used. We will go through different types of data structures.

There are two basic data types:

Primitive Data Structures

Primitive Data Structures are the basic data structures that act directly on computer instructions. The primitives are the basic building blocks of data that do not require a huge quantity of data to be represented. These are pure integrals with predefined behavior and specifications. Integers, Floating Points, Characters, Strings, and Pointers are all represented by them. 

Non-Primitive Data Structures

These are the Data Structures in which data analysts perform all the major operations like – sorting, merging, and many more. Non-Primitive Data Structures are built using primitive data structures. They too are provided by the system itself yet they have derived data structures and cannot be formed without using the primitive data structures. 

Types of Non-Primitive Data Structures are:

Linear Data Structures


Arrays are a simpler data structure. It is a finite group of data that is a homogeneous and contiguous collection of data types of the same type. Before going on to other structures, data analysts should know array construction.

Insertions and removals in arrays are complicated because elements use a static memory allocation approach. Items of the same type are stored at adjacent memory locations so that the position of each element may be easily determined or accessed using an index.


Stack is one of the types of data structure that is similar to an array in that it has an ordered collection of data elements. Push and Pop are the operations of entering or retrieving data from a Stack.

To insert a value into a stack, we use the Push operation, and to recover or read a value from the stack, we use the Pop action from only one end. For storing and retrieving elements, the stack employs a “LIFO” mechanism. 


A Queue is one of the types of data structures in which we may input or add the value from one end called the Rear End and access the value from the other end called the Front End.

The “ENQUEUE” and “DEQUEUE” operations are used to insert and delete elements from the queue. A queue is a collection of sequentially arranged stacks; however, queues only use the “FIFO” technique for storing and retrieving elements. Queues are a type of linear list.

Linked Lists

A Linked List is one of the types of data structure with numerous nodes. Each node consists of a Data Item and a Pointer that provides the address of the next node and is used to point to the next node. 

A linked list differs from an array, in that the order of the list’s members is not defined by a contiguous memory allocation. Linked list data structures are ideal for inserting or removing specific data pieces without reshaping the rest of the list.

Hash Tables

A hash table is one of the types of data structures capable of mapping ‘keys’ to ‘values,’ and many high-level programming languages abstract it and improve it with additional behaviors so that it acts like an ‘associative array’ abstract data type.

It can be used as a linear or nonlinear data structure. A hash table converts an index into an array of buckets containing the requested data item using a hash function.

  • Non-linear data structures


A Tree is one of the types of data structures that stores its items hierarchically, with a root value and child subtrees (with a parent node), the bottom nodes are referred to as “leaf nodes.”Tree data structures are made up of nodes that are connected in a specific pattern, and they (especially binary trees) make it easier to search for data objects.


A Graph is one of the types of data structures that is made up of a finite number of nodes, also known as vertices, and the lines that connect them, which are also known as edges. A network is represented by the Graph data structure. It is made up of vertices and edges (to connect the vertices). These can be used to illustrate real-world systems like computer networks.


A Trie, also known as a keyword tree, is one of the types of data structures that holds strings as data elements that can be arranged visually in a graph. Tries are data structures that hold strings as data pieces in a visual graph. Attempts are also known as keyword trees or prefix trees. You may see the Trie data structure in action whenever you use a search engine and get autosuggestions.

A data analyst should choose a suitable data structure for solving a certain problem, which can be very advantageous and can help minimize the program’s complexity. It entails comprehending complex procedures that have several processes running concurrently and bearing this in mind, you must create a simple approach of practicing repeatedly. To tackle that & be a better analyst, we, at Praxis Business School, a premier B-School based in Kolkata & Bangalore provide Industry driven 9 months Post Graduate Programs in Data Science. We also have a well-structured campus placement program that ensures interview opportunities with the most significant companies in Data Science.

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