What are Data Structures? An Easy Guide In Just 4 Points


Today, due to the advent of technology, a huge amount of data is created and stored regularly in digital format all over the world. It becomes difficult to fetch the required data from the entire pool and present it in an understandable and usable manner to the client or the user. Let’s compare the situation to a supermarket which is not at all well-organized. No staff knows where the commodities are placed, and the entire place seems a mess. Now isn’t it difficult for a customer to find out required commodities and it is also difficult for the storekeepers to keep track of inventories? The same happens with an unorganized set of data. Here comes the role of ‘Data Structures’.

What are Data Structures? They are methods in which data is stored and organized efficiently so that it could be fetched whenever required.

  1. What are Data Structures?
  2. How are Data Structures used?
  3. Choosing the best Data Structure
  4. Types of Data Structure

1) What are Data Structures? 

In the digital world, it’s imperative that data is not only identified and acquired but is also arranged in a particular fashion that makes sense and easy recovery of data possible. This task is done through data structures. Data structures mean the ways data is organized and stored in memory. By applying these data structures, data is organized to form ‘information’ so that it is presented as required by the user.

2) How are Data Structures used?

Data structure forms part of the implementation phase of the programs in the digital world. They help to store and retrieve information stored in primary as well as secondary memories. Data structure form an essential and integral part of programming. Algorithms do this. Algorithms are the finite set of instructions used to carry out a specific task and get the desired output. Irrespective of the programming languages they are designed, they can be used with multiple programming languages. Algorithms paired with data together make up a program.

Usage of Data Structure involves the following terminologies:

  • Data: These are the basic values or collections of values. 
  • Group Items: This includes a grouping of subordinate data items. 
  • Record: This is the collection of various data items that can be grouped.
  • File: A File is a collection of various records of one type of entity.
  • Entity and Attribute: An entity contains various attributes, where the entity represents the class of Objects, and each attribute represents a particular property of that entity.

Operations to be performed on Data Structures.

  1. Searching: Any element can be effectively searched in the data structure.
  2. Sorting: This helps to sort the data elements in either ascending or descending order.
  3. Insertion: Any new element can be inserted into the data structure 
  4. Deletion: Unrequired elements can be trashed off through this operation.
  5. Modification: Elements can be updated as required and also already existing elements can be replaced by a new one.

3) Choosing the best Data Structure

A different set of information requires different types of Data Structures. For example, preparing a register for students requires that the names of students are listed with proper numbers. Similarly, where an Organization structure is to be shown, a Tree Chart forms the best tool. 

The same applies to Data structures. Data Types are variable types. They help to identify what data you are working with. And the associated behaviour or operations you are working with.

Abstract Data Type (ADT) means storing data so that it helps the use of the data in the future. ADT gives the layout, and Data Structure defines how to implement this layout. To decide upon the best data structure for an ADT, ‘time complexity’ and ‘space complexity’ are analyzed. The best one is one where it takes the least runtime and occupies the least space.

Following are the types of Data Structures that depend on the data types and the expected outcomes.

4) Types of Data Structure

Primitive Data Structures: These data structures can hold only values. A one-to-one relationship is maintained between data values. These can be listed below.

  1. Int: Int refers to an Integer. It is a 32-bit signed two’s complement integer, with a minimum value -231 and a maximum value of 231-1.
  2. Float: It is a single-precision 32-bit IEEE 754 floating-point. This data type is never used for precision.
  3. Char: The char data type is a single 16-bit Unicode character. It has a minimum value of ‘\u0000’ (or 0) and a maximum value of ‘\uffff’ (or 65,535 inclusive).
  4. Double: The double data type is a double-precision 64-bit IEEE 754 floating-point. For decimal values, this data type is generally the default choice. As mentioned above, this data type should never be used for precise values, such as currency.
  5. Pointer: A Pointer is used to denote the address of the value stored anywhere in the computer memory thus eradicating repetitive tasks. The process of pointing to an address is called referencing, and the process of obtaining the address through the pointer is called dereferencing.

Non-Primitive Data Structures: These data structures maintain one-to-many relationships between the data values. Under Non-primitive data structures, further two classifications exist. Linear and Non-Linear.

Linear: Here, the data is ‘Sequential’. A one-to-one relationship is maintained between data values. Elements are stored in a non-hierarchical manner wherein each element has a predecessor and a successor except for the first and the last one. Following are the types of linear data structures:

Arrays: An Array is a collection of similar types of data items, and each data item is called an element of the array. These elements share the same variable name but different index values.

For example, Player[1], Player[2], Player[3],……

  1. Lists: This denotes a collection of nodes represented as a list in the memory. Each node points to the next one in the list. A linked list is a list containing data nodes, and each node is connected to the other using a pointer. These contain two elements, one is the data part, and the other is the address.
  2. Single-linked Lists: Single-Linked lists provide for a single pointer, i.e. one pointing to the node and its address.
  3. Double-linked lists: These use pointers to point both previous as well as successor addresses.
  4. Circular-linked Lists: A circular-linked list is nothing but a list whose end node points back to the beginner node, thus forming a loop structure. Thus it has no start and end nodes.
  5. Skip-list: A skip-list allows the efficient viewing of the entire list by skipping a group of elements to manage timelines.
  6. Stacks: In stacks, insertion and deletion are allowed only at one end. The term is derived from its literal meaning. For example, where books have been piled on the table, they can be picked up only from the top end. That means by way of the LIFO (Last In First Out) technique. This technique is used in multiple areas of programming like the appearance of the browsing history or recently used applications on the systems, etc.
  7. Queues: Elements can be inserted only at one end called Rear and deleted at the other called front. FIFO (First In First Out) method is used. For example, maintaining playlists in media, auto-play functions for videos or audios, queueing documents for printing or faxing.
  8. Map: It is a technique where a key is assigned to each value by which it is identified. Such a Key-Value pair is located back whenever needed.

Non-Linear: Here, there is a One-to-many relationship defined between data values. Following are the types:

  1. Trees: These are the multilevel data structures with a ‘root node’ and multiple ‘leaf nodes’ connected to these. They depict the Parent-Child relationship between the nodes where each node can have more than one child nodes, and each child node can have at most one parent node. Except for the root node(a) Binary: In a binary tree structure, each node has at most two children(b) Binary Search Tree: It is more value-based, where the left sub-tree has a value lesser than the right sub-tree and the right sub-tree has a value greater than or equal to the root sub-tree.
  2. Graphs: Graphs denote pictorial representation of elements connected by the links known as edges.


Data Structures are essential for organizing all digital information, so choosing the right one is a crucial part. Understanding of Data Structures can be used in many fields as below:

  • Determining run-time for data structures, given specific algorithms
  • For data science works
  • For software engineers and IT professionals in their day-to-day work.
  • Data Analysis and Data mining
  • Artificial Intelligence

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 


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