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

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  Arrays

In an Array Data Structure, a collection of data elements are accumulated at adjacent memory locations. The basic idea behind this data structure model is to store similar types of multiple elements together, making it easier to calculate the position of each data element. You can add an offset to a base value to get the desired location. The base value is the memory location of the first element, typically denoted by the name of an array.

Linked Lists

Linked lists are also a type of linear data structure like arrays. However, this data structure model does not store data elements at contiguous locations. Instead, the data elements are linked using pointers and include a series of connected nodes. Each node stores the address of the next date alongside the data.

Stacks and Queues

The elements in Stack Data Structures are stored per the LIFO (Last In, First Out) principle. In contrast, Queue Data Structures operate similarly to Stack Data Structures by storing elements according to the FIFO (First In, First Out) principle.

Trees

Trees data structure is a non-linear data structure that uses a collection of data elements known as nodes by linking them together to simulate hierarchy. The data elements are not stored sequentially. Instead, they are stored on multiple levels in a hierarchical structure.

Graphs

Graphs data structures are another type of non-linear data structure possessing a finite number of edges and vertices. The data elements are stored in vertices, and the edges represent their connections. This type of data structure is used to denote the network system.

Hashing

Hashing in a data structure is a technique of mapping keys and values in a hash table by using a hash function. The mapping efficiency is directly proportional to the efficiency of the hash function used. This process helps in faster access to data elements.

Bitwise Operation

The Bitwise Algorithms are mainly used to manipulate bits in various ways and perform operations at the bit level. They are typically swift and frequently boost a program's efficiency.

Algorithms

The procedure of performing a computation or solving a problem is known as an algorithm. It serves as a detailed list of instructions to perform defined step-by-step actions in hardware or software-based routines. Algorithms are widely used throughout the different fields of the Information Technology (IT) sector.

Abstract Data Types (ADTs)

An abstract data type is an abstraction of a data structure. It delivers the interface only to which a data structure must adhere. No specific details are provided regarding the programming language or how it should be implemented.

Binary Tree

A Tree Data Structure having at most two children is known as a binary tree. The elements are named right and left child. A Binary Tree node contains the following parts.

• Data
• Pointer to left child
• Pointer to right child
• Double Linked Lists

A linked data structure consisting of a set of sequentially linked nodes is known as a doubly-linked list in computer science. Each linked node comprises three fields:

• 2 link fields
• 1 data field

In our digital age, programming is a highly in-demand skill for all data analysts and software engineers. Data structures and algorithms form the fundamentals of programming. Hence, the high demand for data structures and algorithms courses among professionals and fresh job aspirants. Data structures are essential when it comes to solving a multitude of real-world complexities. Hence, all tech giants, like Apple, Microsoft, Google, Facebook, and Amazon, as well as product-based companies, focus on measuring the potential recruit’s expertise and knowledge regarding data structures and algorithms. A comprehensive understanding and a sound skill set in data structures and algorithms can increase your chances of getting hired by your dream company.

Most aspirants tend to go for online data structure courses instead of offline ones for many reasons. Online data structure courses can help them study at their own pace and in the comfort of their homes instead of going at a fixed rate of offline data structures and algorithms tutorials. Moreover, offline courses demand physical presence, transportation expenses, and time. All these might pose a problem for aspirants already studying for another degree or students working part-time. Online data structure courses offer you flexibility in time, and you can go through the resources at your own pace. Moreover, most online data structure courses are also cheaper than offline ones.

Different data structures and algorithms tutorials have their own set of prescribed syllabus and course materials for teaching data structures. Students can also read up about the syllabus online on their websites or check the books prescribed by their data structure course experts. We have provided a basic syllabus that all students looking to learn data structures and algorithms can follow:

• Basic Data Structures: Arrays, Strings, Stacks, Queues
• Asymptotic analysis (Big-O notation)
• Basic math operations (addition, subtraction, multiplication, division, exponentiation)
• Sqrt(n) primality testing
• Euclid’s GCD Algorithm
• Basic Recursion
• Greedy Algorithms
• Basic Dynamic Programming
• Naive string searching
• O(n log) Sorting
• Binary Searching

A 2018 report by Deloitte Access Economics forecasted that a massive 76% of businesses are planning to increase their expenditure to expand their data analytic capabilities within the next couple of years. Studies show that the global data science market is estimated to grow to USD 115 billion in 2023 with a Compound Annual Growth Rate (CAGR) of 29%. Almost all industries will benefit from this growth of data structure industries, with the medicine companies, retail, transportation service providers, banking and finance, and education sectors standing to profit the most.

Data has bypassed oil and gold to become one of the most powerful commodities in the 21st century. With that, data science, analysis, and data structures play a major role in providing marketing and operational insights to any company. Data-driven insights address multiple concerns and help boost revenue and capture new markets. As all major business organizations are leaning toward Artificial Intelligence (AI), Machine Learning, and Big Data, the demand for data analysts and data structure specialists has reached sky-high in India. Since 2012, the data science sector has witnessed an impressive hike of 650%, surpassing all other sectors. Hence, there is accelerating demand for data structures and algorithms online courses in India as many young professionals want to tap into this sector packed with high potential.

According to AmbitionBox, the salary of a data structure specialist in India varies from ₹2.3 Lakhs to ₹23.0 Lakhs. The average annual salary is estimated to be ₹7.5 Lakhs and the average estimated take-home salary of a data structure specialist is ₹55,730 - ₹57,127 per month.Factors on which Data Structure Specialist salary in India depends

Almost all salaries in India depend on some specified factors and that of a data structure specialist is no different. The top factors influencing the determination of the salary of a data structure specialist in India are:

• Ability to Pay
• Overall Market Rates
• Cost of Living
• Supply and Demand
• Trade Unions and Government Regulations
• Experience and Productivity
• Training Expenses

Data Structure Specialist Starting Salary in India

The starting salaries depend on location and prevailing market rates, with them differing across companies. The starting salary of a data structure specialist in India is around ₹2.3 Lakhs per annum according to AmbitionBox.