Whether you are a developer or a wannabe data scientist, you need to understand the underlying Data Structures. They are the components that facilitate the creation of fast and powerful algorithms while helping you manage and organise data.

Data Structures are the building blocks of any software, application, or data science process. And choosing the appropriate Data Structure for a task or program is a challenge for any IT professional. Basic knowledge of Data Structure is most important for any data scientist or data analyst.  A Data Science Bootcamp is the perfect way to enhance your knowledge and skills in Data Structures and their role in algorithms.

What is a Data Structure?

Data Structures are used to design software systems and form the base for specific algorithms that are built upon them. They are a collection of data values that enable access, modification, and efficient storage and organizing of data. Examples of Data Structures are arrays, stacks, queues, etc. They are present universally in Compiler Design, Operating Systems, or Graphics. 

Data Structures are stored in memory and are intrinsic to Computer Science algorithms, helping programmers handle data competently.  They affect the performance of software or applications as the speed of data retrieval depends upon how the Data Structure is stored.

Data Structures are values that have relationships between them and allow functions to be applied. Each Data Structure is different in the context of what it is capable of and its specialized application. They are essential elements in the creation of high-speed and powerful algorithms. They aim to organize data so that code is cleaner and easier to access and understand.

Regardless of what operations you are doing on a system, there is always an underlying Data Structure. And you use different types of Data Structures for various purposes, depending on the application. For instance, word documents have a different Data Structure than spreadsheets, and databases use Data Structures different from compilers.                   

To be clear, the Data Structure is not a programming language like C or Java, but a set of algorithms that is applied in a programming language to arrange and store the data in the memory. It uses a set of abstract rules to arrange the data in memory.

Before delving further into the types of Data Structures, we must talk about the abstraction of Data Structures.

What is Abstract Data Type (ADT)?

An abstract data type (ADT) is an abstraction of a Data Structure that provides the interface but no details about how to implement a task or which programming language to use. ADT is a theoretical concept allowing different programming languages to be implemented in various ways.

What are the different types of Data Structure?

In a Data Structure, the data is organized and stored data in the computer in a manner that it can be used efficiently. Array, Queue, Tree, or Graph are Data Structure types that store data in a particular way for easy access and use.

There are generally four types of Data Structures, as given below:

  • Linear: arrays, lists
  • Tree: binary, heaps, etc.
  • Hash: distributed hash table, hash tree, etc.
  • Graphs: directed, acyclic, etc.

An array is a finite collection of data, allocated with contiguous memory locations, where each element is accessed via a numerical index key.

A linked list is a Data Structure where the order of the elements is not stored contiguously but sporadically in a linear order. Elements are called nodes and consist of two parts; the data and the pointer.

 A tree is a hierarchical Data Structure where data is arranged in a hierarchical order and interconnected. A tree has nodes with values connected by an edge, representing the relationship between the nodes. 

A hash table is a Data Structure that connects keys to abstract values.

A graph is an abstract non-linear Data Structure that executes the principles of graph theory and consists of nodes and edges.

What are its advantages?

Different types of Data Structures have various advantages depending upon their arrangement and application.

Some advantages are common for all types of Data Structures, as listed below:

Secure Storage and access

Data Structures store information securely on computer systems. Storage of data in memory allows easy and quick access at any time.

Storage on hard disks

Data Structures also allow information to be stored in hard disks, making it convenient to be accessed from anywhere.


Data Structures are reusable, which essentially means that multiple client programs can use the Data Structure. Once a specific Data Structure is implemented, it can be used in other places in the system and by different clients by compiling into libraries.

Capacity to process on a software system

Data Structures facilitate the use and processing of data on a software system. For instance, logging work hours or producing a report remotely.


Data Structure specified by an Abstract Data Type (ADT) provides abstraction as the internal working of the Data Structure cannot be seen. The client program can only use the interface without worrying about the implementation specifics.


The choice of Data Structures decides the efficiency of a program. For instance, if data is arranged in an array form, search functions will be cumbersome. However, other Data Structures like binary search trees or ordered arrays can make the search process more efficient and quick.

Data management

Data Structures enable the management of large datasets more efficiently. The processing and manipulation of large amounts of data become convenient and fast with a robust Data Structure approach.

Aids programming 

Data Structures are necessary for the design of efficient algorithms. Using the appropriate Data Structure can increase the efficiency of operations such as storage, retrieval, or processing of data. Most Data Structures are well-organized for popular functions such as storage, retrieval, manipulation, deletion, etc., and allow the programmer to rely on these Data Structures.

Allows direct use

As Data structures are well tested and proven, programmers and users can use them directly without spending time researching and developing. 

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