Types of Data Structures: Enhancing Your Coding Skills

When preparing for an interview, computer programmers may need to examine several types of data structures. A data structure is a method of successfully managing and arranging data to facilitate retrieval. Understanding how data structures function helps show employers that you know the foundations of programming.

In this post, we will explore data structures, explain why they are essential, and present a list of nine data structures that every programmer should be familiar with.

What is a data structure?

A data structure is a way to organize and store data in computers. It depicts a set of data values, their connections, and the actions or activities they may perform. Data structures are used by computer programmers to transmit data to other application components or to create a new application. Data structures have four fundamental functions: input, processing, maintenance, and retrieval.

Why are data structures important?

Data structures are an essential part of computer science since they enable experts to store and handle enormous datasets. Using an effective method allows you to readily obtain information. Employers frequently question candidates about data structures during computer science interviews to demonstrate their understanding of the fundamental starting point for programming. It's also useful in other fields, such as artificial intelligence (AI), graphics, and operating systems.

Types of data structures

Computer programmers can employ a number of data structures based on the work at hand or the application being used. Here are some popular data structures you might use in software engineering:

1. Arrays

Arrays group related elements together. This structure organizes data by allocating contiguous memory blocks. When utilizing an array, each element is identified by at least one array index or key. An array is the foundation for other data structures like hash tables and lists. Computer scientists frequently utilize this structure for sorting algorithms.

2. Stacks

Stacks have a last in, first out (LIFO) structure, which means that the computer organizes prior work such that the last action appears first. For example, if you input the dataset as "1, 2, 3, 4," the last digit, "4," will display at the top. This data structure results in a pile or stack of information. A stack data structure is useful for organizing information where the sequence of activities is critical. This structure's design ensures that you finish your duty before moving on to the next.

3. Queues

Queues, unlike stacks, use a first in, first out (FIFO) structure to organize data. This linear structure is similar to a waiting queue in that information enters and waits to be produced. The information typed initially is the first to exit the line. Computer programmers use queues to arrange data that does not need to be processed right now.

4. Linked lists

Linked lists arrange items, or nodes, in a linear order depending on their relationship to each other. Each node consists of data and a pointer. The data is what the programmer supplied to the node, while the pointer refers to the next node in the series. Linked lists are ideal for scenarios requiring the ability to remove data. They can also help you set up stacks and queues.

5. Binary trees

A binary tree is a nonlinear structure composed of nodes with two possible values or orientations. The top node, or root, has two children: one right and one left. There are several varieties of binary trees, including:

• Rooted binary tree: Rooted binary trees have a root node, with every node having up to two children.
• Full binary tree: This type of binary tree occurs when every node has either zero or two children.
• Perfect binary tree: In a perfect binary tree, all interior nodes contain two children, and all external nodes, or leaves, have the same level.
• Complete binary tree: Complete binary trees occur when all levels except for the last are completely filled and nodes are located as far left as possible.
• Balanced binary tree: Balanced binary trees are ones where the left and right children's heights are different by at least one, the left child has a balanced amount and the right child has a balanced amount.
• Degenerate tree: In a degenerate tree, each parent node only has one child, representing a linked list.

Binary trees are useful for representing structural relationships in data. They can also aid to depict hierarchies.

6. Graphs

Graphs are nonlinear lists that depict networks. They are made up of nodes and edges that link each other. These structures employ a pair, X and Y, with the X vertex linking to the Y vertex. Graphs are excellent for analyzing networks, such as a city route or a social media network.

7. Tries

Tries, often known as "prefix trees," are tree-like data structures for storing data. They are commonly used to represent alphabetical words. Nodes in the tree are strings that programmers may retrieve by moving down the branch. Tries are handy for arranging data according on the prefix of a string. Tries are commonly used to provide auto-suggestions and seek up words in dictionaries.

8. Hash tables

Hash tables, often known as maps, hold key-value pairs. They generate an index, or hash code, into the slots where the requested value is stored. Computer programmers store information in an array structure. They can employ hash tables to create association arrays, database indexes, and set data structures.

9. Skip lists

Skip lists are probabilistic data structures that list elements in the form of a linked list. This sort of structure is known as a skip list since it skips certain entries from a whole list. Each incremental level in a skip list has fewer components, but no new ones. Skip lists are useful when programmers need to remove, insert, or search for information fast.