DSA Types

DSA is all about the methods for organizing and storing data and the design of procedures/functions/algorithms for solving problems.

DSA consists of two parts:

• Data Structures
• Algorithms

In this continuation blog, I will focus on Data Structures and different types.

The definition for Data Structures can be explained as "The study of storing and organizing data in computers." It is a way of arranging data on a computer to be accessed and updated efficiently. That simplifies the processing, retrieving, and storing data on digital platforms. The idea is to reduce the space and time complexities of different tasks.

Data Structures can be divided into 2 types: Primitive and Non-primitive Data Structures

Primitive data structures are simplest form data types, supported by computer systems. These can be assumed as built-in types. These can hold a single value with specific data type.

• Ex: int, char, float, double, and pointer.

Non-primitive data structures are complex data types. These can hold multiple values. Assume these as user-defined types.

Non-primitive are further classified into 2 types:

• Linear Data Structure and Non-linear Data Structure

Linear Data Structure are the data structures in which data elements are arranged sequentially or linearly, where each element is attached to its previous and next adjacent elements.

These can be treated as Static or Dynamic types.

Static data structures has a fixed memory size. It is easier to access the elements in a static data structure.

• Ex: Array.

Dynamic data structures do not have a fixed memory size. It can be randomly updated during the runtime which may be considered efficient concerning the memory (space) complexity of the code.

• Ex: Queue, Stack.

Non-linear Data Structure are the data structures where data elements are not placed sequentially or linearly. Here all the elements cannot be accessed/traversed in a single run in a non-linear data structure.

• Ex: Trees and Graphs.

Finally a tree diagram to summarize the different Non-primitive data structures.

Explore each of these data strcutures and learn which type of data structure will suite your requirements for effective and optimized programming.