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Understanding the Types Data Structures and Their Applications


The advance and handy use of cell phones and its applications in life is completely dependent on technology. What is data? Data is information optimized for processing and movement, facts and figures stored on computers. To access this data, you need data structure. So, what is data structure? Data structure is a systematic way of storing & organizing of data in the computer so that it can be used efficiently, and includes data structure types.

Types of Data Structures are basically a scheme for organizing data in computer memory in such a way that it can quickly help in accessing the data to the processor for required calculation. Algorithm are the rules and instructions that turn the data into something useful for programming. Types of data structures are defined as addressing its functions as ‘ADT’, (Abstract Data Type) which means it is independent of implementation. Let’s better understand the major types of data structure and their fubctions.

Characteristics of Data Structures

Lets explore the major characteristics of data structures of data structure in detail.

  • Linear & Non-Linear Types Data Structure:

    Linear and non-linear data structure type arranges the data in sequential order such as graphical representation, arrays, etc.

  • Static & Dynamic Types of Data Structures:

    Static data structure type has characteristics shows the compilation of data. It has fixed format and sizes along with memory locations.

  • Time Complexity Types of Data Structures:

    This type of data structure maintains the time punctuality of program execution. The accuracy of the device depends upon the time taken by the program for execution. The less the time taken for execution the more accurate the device.

  • Correctness:

    It is highly important characteristics of data structures to have an interface for each data. Data Structure should be accurately implemented in the interface. The interface depicts the set of data structures.

  • Space Complexity:

    Space complexity ensures the proper management of the space used by the data in the device. The memory usage should be less which indicates the proper function of the device. Thus this is the important characteristics of data structures for space management.

Types of Data Structure

Data Structure Types and Classifications

Data structures are grouped based on the type of operations required to perform. The two broad types of the data structure are Primitive data structure types and non-primitive data structure type.

  • Primitive Types of Data Structures:

    The primitive data structure is also known as built-in-data structure. These types of data structure directly operates according to the machine instructions. Below is the list of major types of primitive data structures.

    • Boolean Data Structure Type:

      In Boolean data type the data can either be true or false or positive or negative. It shows whether the data is valid or invalid. In Boolean data structure type the binary variables, logical values and algebra is used.

    • Integer Data Structure type:

      In integer data structure type storage for both positive & negative numbers along with zero is made available. All the arithmetic operations can be efficiently done through integer data types. 

    • Float Data Structure type:

      Approximate values are used in a form of formula in order to allow a trade-off between range and precision. 

    • Character Data Structure type:

      Character data type is used to store information in a fixed-length field or variable- length string. Fixed-length is used by the CHAR data type and are right extended with spaces on output. Variable – length string is not extended. 

    • Pointer Data Structure type

      A pointer type of data structure is a variable that stores the memory address of another variable as its value. Arrays or data objects are stored in such alliances.

  • Non-Primitive Types of Data Structures:

    The non-primitive types of data structure are also known as derived types as they are are basically derived from primitive data structure and are comparatively more complicated.  The primitive data structures are further classified into Linear and non-linear data structure types.

Linear types of data structures & Non-Linear Types of Data structures

Get detailed information on types of data structure including linear data structure and non-linear data structure with an-overview-of-data-structures-and-algorithms-for-beginners.

Linear Types of Data Structures

In this type of data structure, the values are arranged linearly in a way that each value is linked to the previous value. The popular linear types of data structures are Array, Stack, Queue and Link list.

  • Array Data Structure:

    Array types of data structure are collections of data items that are of the same type, stored together in adjoining memory locations. Each data item is known as an “element.” Arrays are the most basic, fundamental data structure. It is important for Data Scientists to master the skill of array construction before moving on to other structures such as queues or stacks. The syntax to create an array depends upon the programming language one uses.

  • Features of Array Types Data Structure:

    • An element types. All elements of an array are of the same type.
    • An index ranges. Elements of an array are accessed using an index value. Each array element has a unique associated index value.
    • A length. The length of the array is the number of elements in the array.
    • A size. The size of the array is the size of all the elements in the array. This can be expressed mathematically as Length * Element size where Length is the number of elements in the array and Element size is the memory size of an individual element.
    • A name for the array object.
  • Application of Array

    • Array stores data in tabular format. It is used in storing contacts in the phone memory, storing examination marks, daily weather report, etc.
    • Used to store data in ascending and descending order.
    • Used to represent graphs in computer science. Each element in the array represents a node in the graph, which represents the relationship in the graph.
    • Used in financial analysis to store historical stock prices and other financial data.
    • Used in scientific computing to represent numerical data, such as measurements from experiments and simulations.
  • Data Structure Examples of Array

  • Linked List Data Structure Types:

    The linked list types of data structure are the most preferred data structure types when it comes to handling dynamic data elements. 

    Item contents are stored in linear order in linked list type of data structure.

    Each element contains data item and a link or reference to the subsequent item on the same list.

  • Stack & Queue Types of Data Structure:

    Stack data structure follow the LIFO system for fetching the data, i.e. the top element can be retrieved by popping the data using the pop () method.

    Queue data structure follow the FIFO system. The oldest element is retrieved by calling dequeue ().

    It can be implemented using an array or a list.

Non-Linear Types of Data Structures

Types of Data Structure
In this type of data structure, the values are arranged at multiple levels into a non-linear manner. It is more complicated than the linear data structure where each value is connected to two or more value. The primitive data structures examples are tree and graphs data structure.

  • Tree Data Structure:

    A tree is a subtype of graph data structure that typically imposes the rule that nodes of the tree do not create loops in the data structure. A common use is a binary search which can retrieve data in binary time of O (log N).

  • Graph Data Structure:

    A graph is a node-based type of data structure that contains a list of other nodes that are linked in the Graph. 

Data Structure Examples: A data structure used to represent cities connected to other cities.

Importance of Data Structures:

  • Efficient organization of Data – It is a types of data structure allow you to store and retrieve data in an efficient and quick manner.
  • Simple Understanding – Various types of data structure help in better understanding the code.
  • Improved performance – the types of data structures can help improve the performance of your code by reducing the time and space complexity of algorithms
  • Bulk handling of Data- processing an application of thousands of users on the web browser.
  • Huge career opportunities- learning the types of data structure and algorithms is important when one aspires to work with big companies like Google, Microsoft, etc. Check out the scope of data engineering in India.

Best Path for Data Science Professional

As Sir George Couros rightly said “Technology is not just a tool. It can give learners a voice that they may not have had before.” The data science domain is booming. Careers in data science are in demand, and data scientists are no longer restricted to only information technology. From health care, retail market, supply chain management, entertainment, transportation, government organizations, etc. everywhere there is need for Data Scientists. Check out the important data engineering interview questions to start your preparation today.

For all aspiring data scientists, our data science and machine learning course might help you take the leap.


Data Structure is a scheme for organizing data in computer memory in such a way that it can quickly help in accessing the data to the processor for required calculation. Learn all about to how become data science engineer and get a saucerful career option.
The average salary of a data scientist in India is approx. Rs.11 lakh/annum. Check out how much you can earn as a data scientist in India.
An algorithm is a sequence of steps executed by a computer that takes an input and transforms it into a target output. This is used to help in retrieve data from the system in a systematic way.
The LIFO system is used for fetching the last fed data i.e. the top element can be retrieved by popping the data using the pop () method.

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