Every Programmer Should Be Aware Of The Following Eight Data Structures.

 The most crucial subjects for programmers are algorithms and data structures—algorithms and data structures are, in a way, a programmer's bread and butter. If you want to enter the field of programming and get started straight away, you'll need to understand the most common data structures and fill your résumé with in-demand skills.

We'll look at the eight most important data structures that every programmer should be familiar with, as well as what they do and how to use them. Let's start with a basic knowledge of what a data structure is.

What is a Data Structure?

Data structures are ways of storing and organizing data in a computer system so that actions on it may be performed more quickly. When data is "unstructured," it lacks a clear data model or is not organized in a way that makes operations or analysis easier.

Unstructured data is a prevalent issue in businesses that have gathered data but have not successfully stored or organized it. Unstructured data accounts for around 80% of all data on the planet.

Different layouts exist for data structures, each of which is efficient for some operations but inefficient for others. The programmer's job is to figure out which data structures are appropriate for the data at hand so that they may be used to solve problems.

Eight Data Structures

Some of the most important data structures to be aware of are listed below. This isn't an exhaustive list, and you can play around with other data structures to come up with your own. However, these are the fundamentals that will enable you to pursue a career in programming and data analysis.

1. Arrays

An array is a collection of elements stored consecutively and is one of the most basic data structures. You can't adjust the size of an array since it contains values or variables of the same data type (known as "elements"). An array's items are indexed from zero to one.

Consider an array in the same way that you would a weekly pill organizer. It consists of a series of miniature containers, each with its own set of elements.

2. Linked Lists

A linked list is a collection of objects that are related to each other in a linear order. This means you can't access data at random because you have to access it in order.

A "node" is the name given to each element in a linked list, and each node has a key and a pointer. The "next" node is indicated by the pointer. The sequence begins with a "head," which points to the list's first item. The "tail" is the final item on this list.

You can make a singly linked list, which allows you to traverse each item from the head to the tail in a forward way. A doubly-linked list, on the other hand, can be traversed both forward and backward. Finally, you can make a circular linked list in which the tail's next pointer points to the head, and vice versa, completing a circle.

When moving between programs with Alt + Tab, linked lists are utilized to maintain symbol tables (On a PC).

3. Stacks

The function of a stack is almost identical to how it sounds. It's like putting things in a tall container and piling them up.

LIFO (Last In First Out) structures are commonly used to describe stacks. This indicates that the element that was set last can be accessed first. You can either "push" a new element to the top of the stack or "pop," which deletes the element that was put previously and is at the top.

Stacks are frequently used in recursion programming to parse and evaluate mathematical equations, as well as to execute function calls.

4. Queues

A queue is similar to a stack, however, it is a FIFO (First In First Out) structure rather than a LIFO (Last In Last Out) structure. Consider a line of people waiting to enter a building. The person at the front of the line will be the first to enter the building, while the one at the back will be the last to enter.

In this structure, you can enqueue an element, which means adding it to the queue's end. You can also remove an element from the queue by dequeuing it.

Queues are frequently used to manage threads in multithreading, and they are also utilized to implement priority queuing systems (as one might expect).

5. Hash Tables

Each value is assigned to a key and then stored in a hash table structure. This facilitates the use of a key to quickly look up values. It's a quick and easy approach to insert and search for data, regardless of its size, because it makes it simple to distinguish one object from a group of similar ones.

If you attend college, for example, you can be given a unique student ID number. This ID number serves as a key to get information about you and your academic history.

A hash table maps a data set of arbitrary size to a fixed-size hash table using a "hash function." "Hash values" refer to the values that a hash function returns.

Hash tables are frequently used to build database indexes, associative arrays, and "sets."

6. Trees

Because each item is linked, a tree is similar to a linked list. Items in a tree, on the other hand, are linked in a hierarchical manner, similar to what you'd see in a graphic representation of someone's family tree. Trees come in a variety of shapes and sizes, each suited to a specific purpose.

7. Heaps

A heap, on the other hand, is a sort of binary tree in which the parent nodes are compared to their offspring. As a result, the values within the nodes can be ordered as desired. Trees can be used to represent heaps, although binary arrays can also be used to represent them.

Heaps can be divided into two categories. The parent's key in a min heap is less than or equal to its children's keys. The parent's key in a max heap is greater than or equal to the children's keys.

In algorithms, heaps are frequently used to generate priority queues and identify the smallest or greatest value in an array.

8. Graphs

A graph is a non-linear abstract data structure made up of a finite number of nodes connected by edges. The edges are simple lines or arcs that connect two nodes in the graph, whereas the nodes are referred to as "vertices" and carry values.

Networks, such as circuit networks or even city pathways, are frequently represented using graphs. They're useful for addressing problems in the physical world, but they may also be used to symbolize digital networks.

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References:

emerging ad., 2021. Eight Data Structures Every Programmer Has To Know. [online] emerging ad. Available at: <https://emerginged.com/blogs/emerginged-blog/eight-data-structures-every-programmer-has-to-know?ga_client_id=GA1.2.6498158.1645560672&viewId=emergingedl0ecxuisfndecx> [Accessed 23 February 2022].

Vijini, ., 2020. Common Data Structures every Programmer must know. [online] towards data science. Available at: <https://towardsdatascience.com/8-common-data-structures-every-programmer-must-know-171acf6a1a42> [Accessed 20 February 2022].

Shivam Jaiswal, ., 2020. Type of Data Structures. [online] CP. Available at: <https://www.thecoderpedia.com/blog/type-of-data-structures/> [Accessed 20 February 2022].

Writer: Himanshu Sharma