Data Structures Courses

Before we define what a data structure is, we need to have a cursory understanding of what data means. Data is the accumulation of various numerals, symbols, and alphabets to symbolize optimizable information for processing and movement, facts and figures stored on computers.

Data structures are specific methods of organizing these data in a technical format on a computer so that the required data can be processed, analyzed, and retrieved effectively and rapidly. The ultimate aim of data structures is to cut down the space and time complexities of various tasks. Choosing an apt data structure program makes executing a string of critical operations easier. An effective data structure program will require minimum execution time and memory space of computers to perform the task and process the relevant data structure.

All data structures can be divided into two broad categories:

Linear Data Structure

Non-linear Data Structures

Linear Data Structures

In these types of data structures, all the components are organized sequentially, one after another. Since all the elements are arranged in a specific order, implementing them is much easier. However, linear data structures might not be a good choice for complex programs, as they might not be able to tackle the operational complexities.

Some popular linear data structures are -

Stack Data Structures

The elements in Stack Data Structures are stored per the LIFO (Last In, First Out) principle. Let us imagine a stack of plates. It is only natural that the last plate is on the top of the pile and will be removed first. This data structure works in a similar method where the last element stored in the stack gets removed first.

Linked Data Structures

In this data structure model, the data components are connected through a series of nodes. Each node comprises the elements and the address to the adjacent node.

Array Data Structures

The data elements are stored in a serial memory where each array contains the same type of data elements. The programming language determines the data elements to be stored in the form of arrays.

Queue Data Structures

The Queue Data Structures operate just the opposite way of Stack Data Structures. It uses the FIFO (First In, First Out) principle, where the first data component stored will get removed first.

Non-linear Data Structures

Unlike Linear Data Structures, the data elements in Non-linear Data Structures are not arranged in any sequential order. Instead, the data elements are stored in multilevel, and multiple runs are required to traverse through all of them completely. Each data element can have multiple paths to travel to another data element.

The main Non-linear Data Structures are:

Graph Data Structures

This data structure has a finite number of edges and vertices, the former used to connect with the latter. The data elements are stored in the vertices, and edges represent how the vertices are related to each other. The Graph Data Structure represents the network system and its principles in computer networks.

Tree Data Structures

This data structure uses a collection of data elements known as nodes linked together to simulate hierarchy. Instead of storing the data elements sequentially, they are stored on multiple levels in a hierarchical structure. The topmost node is called the root node.

Data structures and algorithms are essential tools for every programmer. While there are many such data structures, some data structure programs are necessary for all programmers to know. All potential programmers looking to enter the field of data analysis need to be familiar with and learn the basic data structures. While you are free to come up with your own data structures running various experiments, you need to learn the data structures mentioned below:

• Linear Data Structures: Linked, Arrays, Queues, and Stacks
• Non-linear Data Structures: Trees and Graphs
• Hash Table Data Structures

In this data structure, each data element is associated with a key before storing them. This makes it an efficient and easy way to insert and search data irrespective of the size. The method also makes it easy for you to identify a specific data set from a group of similar ones. The hash table uses a hash function to map any sized data set to a fixed size - this is called the hash table, and the values that the function returns are called hash values. This data structure is most commonly used to construct associative arrays, database indexes, and sets.

Heaps Data Structures

A heap is similar to a binary Tree Data Structure where the children nodes are compared to their parent nodes and are arranged accordingly concerning the values. Heaps can also be represented as binary Array Data Structures as well. They are commonly used to find the largest or the smallest value in an array or to create priority Queue Data Structures. There are two types of Heaps Data Structures, where a maximum heap is where the parent’s key is greater than or equal to its children’s keys and a minimum heap is where the parent’s key is less or equal to that of its children’s keys.

Well, there is no easy way or shortcut while learning about data structures and algorithms. As a programmer, it is absolutely essential that you make yourself familiar with the above-mentioned data structures. There is no need for you to write your own data structures since most are provided by every major Software Developing Kit (SDK), such as C++Standard Temple Library (STL) or Java Development Kit (JDK). However, a comprehensive understanding of data structures and algorithms is required to know where to use them effectively.

A beginner’s step-to-step guide on how to learn data structures and algorithms has been provided below:

• Making Yourself Familiar With The Topics

The most critical step of learning data structures is to familiarize yourself with the topics and identify the best resources to do so. Before starting to learn, take care in finding the best available resources and the order in which you need to read about the topics. Aspirants can follow the order suggested below while learning about data structures and algorithms:

• Search For the Best Available Resources

Once you have an idea about the topics you need to learn, it is time to look for the best available resources to do so. You can get hold of books, go through data structure and algorithm tutorials available online, and search for the best data structures and algorithms courses online.

• Learn the Fundamentals

It is essential to learn and understand the basics of any topic before trying to master it. Spend time carefully understanding all the fundamentals of data structure and algorithms and practice every step of the process to solidify your learnings.

• Aim for an In-depth Study

After having a comprehensive understanding of the fundamentals of the topics, go for the advanced data structures. Learn about the detailed applications of data structures and algorithms and their implementations. You need to practice every step and take mock tests available online to get the best results out of the learning process.

• Be Consistent in Your Approach

Always remember that consistency is the key. Do not be erratic about the learning process and focus on consistency. Try to follow a consistent schedule while studying data structures and algorithms, take part regularly in online tests, solve problems daily, and practice as much as possible.

Coding interviews play a major role for aspirants looking for jobs in machine learning, software engineering, and data science. While most candidates are adept at using programming to solve daily-life research problems, questions about data structures pose an issue for them. Brush up on the fundamentals of the different data structures for most interview questions center around choosing the correct data structures and algorithms for the given problem. Read up on the basic and advanced data structures, data structure programs in C, and data structures and algorithms in Java and Python.

Some of the most commonly asked topics in interviews regarding data structures and algorithms are:

• Reversing a linked list
• Trapping Rain Water
• Shortest Subarray with Sum at least K
• Longest substring without repeating characters
• Group Anagrams
• Circular linked lists
• Task Scheduler
• Binary Tree Right Side View
• Valid Parentheses