5 Types of Data Structures and Algorithms Every Techie Should Know

Do you wish to acquire advanced computing skills? You must be familiar with the basic algorithms and data structures, including operating systems, networking, and programming languages, depending on structures used in computer science.

Here, we will explore the five types utilized by modern computer scientists and developers.

1.  Linear Data Structures

There are two data structures used in computer science: linear and nonlinear. The most straightforward are linear data structures, which group data into a single level. Each element is directly linked to the items that come before and after it. Think of a stack of cards or a shelf of books.

The array, linked list, stack, and queue types of linear data structures are frequently employed.

• Array

Arrays make it simple to analyze, sort, and search data by storing related data pieces at nearby locations in memory. In practically every computing program, arrays are one of the earliest data structures in computer science.

• Linked List

Each data member in a linked list refers to the one before it. With this configuration, users can efficiently add or remove items from any place in the sequence.

• Stack

The “last-in, first-out” (LIFO) rule groups data components into stacks. A new element added to a stack is stacked on top of all earlier elements. Only the topmost piece in a stack can be deleted. Stacks are the best way to quickly retrieve recently used items or data in the order that it was entered.

• Queue

Data element collections known as queues adhere to the “first-in, first-out” (FIFO) principle. Queues, instead of stacks, arrange new components so that they appear at the bottom of all active elements. Always remove the top item in the queue first.

2. Nonlinear Data Structures

Key ideas in computer science include graph data structures and algorithms. They are a type of nonlinear data structure. They don’t connect pieces sequentially and have numerous levels of data. Consequently, graphs let computer experts use data to tackle challenging issues.

Nodes and edges makeup graphs. Data elements are stored in nodes, and their connections are shown as edges. From electrical circuits to the people in a social network, graphs can display a variety of data interactions.

A tree is a form of a graph that arranges nodes in a hierarchy. The root is the topmost node. Zero or more subtrees are connected to the root. A binary search tree, for instance, has a left and a right subtree that are both connected to the root. In a DSA course, you may find a detailed explanation of data structures. 

3. Sorting Algorithms

Data can be rearranged in arrays and lists using sorting algorithms, which are step-by-step processes. For instance, a user might want to sort an array in lexical or numerical order. Searching algorithms, for example, can operate more quickly and effectively thanks to sorting algorithms.

Three fundamental algorithms for sorting are insertion sort, merge sort and rapid sort.

• Insertion Sort

In tiny data sets nearly sorted, insertion sort is a productive technique to complete the task. Using this approach, sorted and unsorted portions of an array are separated. The process repeats until all the components are sorted, selecting one from the unsorted section and inserting it in the sorted part.

• Merge Sort

The best method for arranging linked lists is merge sort. A list is split into two parts until no more divisions are possible. The elements are then compared and combined again in the same manner in which they were split.

• QuickSort

Quicksort is beneficial for large data sets. It divides an array into two subarrays based on the pivot, a designated data element. Elements in the first subarray have values lower than the pivot. Elements in the second subarray have values higher than the pivot. The algorithm locates the pivot in each subarray up until there is just one element in each subarray.

4. Searching Algorithms

Specific elements within data structures are found and retrieved using search methods. Binary search and linear search are two prime examples.

• Linear Search

A sequential search strategy for sorted and unsorted data structures is called linear search. It moves through lists and arrays sequentially, going through each element.

Consider a list of elements with unsorted values “1” through “25,” unsorted. In a linear search, the value “5” would move through the values in the order they are stored until it was found.

• Binary Search

A method of interval searching is a binary search. Multiple intervals are created for each element in interval searches, which only travel over the desired interval. They split the search space, making them more effective than sequential searches.

Finding items in a sorted list is effective with binary search. The search value is put up against the data structure’s middle element, and after that, the predicted interval is traversed.

Consider an ordered list of elements with the values “1” through “25.” The middle element, “13,” and the value “5” would be compared in a binary search. The program would look for the number in the bottom half of the interval because “5” is smaller than “13.”

5. Graph Traversal Algorithms

In order to search nodes in graphs and trees, computer scientists employ graph traversal techniques. In contrast to linear data structures used in computer science, graphs require many searches to locate and retrieve data.

The breadth-first search and the depth-first search are two graph traversal techniques. They aid computer experts in resolving the most prevalent graph and tree-related issues.

• Breadth-First Search

Breadth-first search (BFS) traverses the shortest path between two nodes. Starting at the tree root, it explores nodes in order of distance.

In a tree with two levels of nodes, BFS would search nodes from left to right in the following order:

• Tree root

• Nodes in the first level

• Nodes in the second level

• Depth-First Search

DFS, or depth-first search, scans graphs from top to bottom. Before turning around and moving on to the next branch, it travels as far as it can down one branch.

Steps: 

• Pre-order traversal

• In-order traversal

• Post-order traversal

Bottom Line

 

If you are tech enthusiast or want to strengthen your DSA skills, Visit Learnbay’s data structures and algorithms course, System Design course in collaboration with IBM. Understanding DSA will help you solve problems easily for your interviews.