Data Structures Tutorial

Data Structures Tutorial Asymptotic Notation Structure and Union Array Data Structure Linked list Data Structure Type of Linked list Advantages and Disadvantages of linked list Queue Data Structure Implementation of Queue Stack Data Structure Implementation of Stack Sorting Insertion sort Quick sort Selection sort Heap sort Merge sort Bucket sort Count sort Radix sort Shell sort Tree Traversal of the binary tree Binary search tree Graph Spanning tree Linear Search Binary Search Hashing Collision Resolution Techniques

Misc Topic:

Priority Queue in Data Structure Deque in Data Structure Difference Between Linear And Non Linear Data Structures Queue Operations In Data Structure About Data Structures Data Structures Algorithms Types of Data Structures Big O Notations Introduction to Arrays Introduction to 1D-Arrays Operations on 1D-Arrays Introduction to 2D-Arrays Operations on 2D-Arrays Strings in Data Structures String Operations Application of 2D array Bubble Sort Insertion Sort Sorting Algorithms What is DFS Algorithm What Is Graph Data Structure What is the difference between Tree and Graph What is the difference between DFS and BFS Bucket Sort Dijkstra’s vs Bellman-Ford Algorithm Linear Queue Data Structure in C Stack Using Array Stack Using Linked List Recursion in Fibonacci Stack vs Array What is Skewed Binary Tree Primitive Data Structure in C Dynamic memory allocation of structure in C Application of Stack in Data Structures Binary Tree in Data Structures Heap Data Structure Recursion - Factorial and Fibonacci What is B tree what is B+ tree Huffman tree in Data Structures Insertion Sort vs Bubble Sort Adding one to the number represented an array of digits Bitwise Operators and their Important Tricks Blowfish algorithm Bubble Sort vs Selection Sort Hashing and its Applications Heap Sort vs Merge Sort Insertion Sort vs Selection Sort Merge Conflicts and ways to handle them Difference between Stack and Queue AVL tree in data structure c++ Bubble sort algorithm using Javascript Buffer overflow attack with examples Find out the area between two concentric circles Lowest common ancestor in a binary search tree Number of visible boxes putting one inside another Program to calculate the area of the circumcircle of an equilateral triangle Red-black Tree in Data Structures Strictly binary tree in Data Structures 2-3 Trees and Basic Operations on them Asynchronous advantage actor-critic (A3C) Algorithm Bubble Sort vs Heap Sort Digital Search Tree in Data Structures Minimum Spanning Tree Permutation Sort or Bogo Sort Quick Sort vs Merge Sort Boruvkas algorithm Bubble Sort vs Quick Sort Common Operations on various Data Structures Detect and Remove Loop in a Linked List How to Start Learning DSA Print kth least significant bit number Why is Binary Heap Preferred over BST for Priority Queue Bin Packing Problem Binary Tree Inorder Traversal Burning binary tree Equal Sum What is a Threaded Binary Tree? What is a full Binary Tree? Bubble Sort vs Merge Sort B+ Tree Program in Q language Deletion Operation from A B Tree Deletion Operation of the binary search tree in C++ language Does Overloading Work with Inheritance Balanced Binary Tree Binary tree deletion Binary tree insertion Cocktail Sort Comb Sort FIFO approach Operations of B Tree in C++ Language Recaman’s Sequence Tim Sort Understanding Data Processing Applications of trees in data structures Binary Tree Implementation Using Arrays Convert a Binary Tree into a Binary Search Tree Create a binary search tree Horizontal and Vertical Scaling Invert binary tree LCA of binary tree Linked List Representation of Binary Tree Optimal binary search tree in DSA Serialize and Deserialize a Binary Tree Tree terminology in Data structures Vertical Order Traversal of Binary Tree What is a Height-Balanced Tree in Data Structure Convert binary tree to a doubly linked list Fundamental of Algorithms Introduction and Implementation of Bloom Filter Optimal binary search tree using dynamic programming Right side view of binary tree Symmetric binary tree Trim a binary search tree What is a Sparse Matrix in Data Structure What is a Tree in Terms of a Graph What is the Use of Segment Trees in Data Structure What Should We Learn First Trees or Graphs in Data Structures All About Minimum Cost Spanning Trees in Data Structure Convert Binary Tree into a Threaded Binary Tree Difference between Structured and Object-Oriented Analysis FLEX (Fast Lexical Analyzer Generator) Object-Oriented Analysis and Design Sum of Nodes in a Binary Tree What are the types of Trees in Data Structure What is a 2-3 Tree in Data Structure What is a Spanning Tree in Data Structure What is an AVL Tree in Data Structure Given a Binary Tree, Check if it's balanced B Tree in Data Structure Convert Sorted List to Binary Search Tree Flattening a Linked List Given a Perfect Binary Tree, Reverse Alternate Levels Left View of Binary Tree What are Forest Trees in Data Structure Compare Balanced Binary Tree and Complete Binary Tree Diameter of a Binary Tree Given a Binary Tree Check the Zig Zag Traversal Given a Binary Tree Print the Shortest Path Given a Binary Tree Return All Root To Leaf Paths Given a Binary Tree Swap Nodes at K Height Given a Binary Tree Find Its Minimum Depth Given a Binary Tree Print the Pre Order Traversal in Recursive Given a Generate all Structurally Unique Binary Search Trees Perfect Binary Tree Threaded Binary Trees Function to Create a Copy of Binary Search Tree Function to Delete a Leaf Node from a Binary Tree Function to Insert a Node in a Binary Search Tree Given Two Binary Trees, Check if it is Symmetric A Full Binary Tree with n Nodes Applications of Different Linked Lists in Data Structure B+ Tree in Data Structure Construction of B tree in Data Structure Difference between B-tree and Binary Tree Finding Rank in a Binary Search Tree Finding the Maximum Element in a Binary Tree Finding the Minimum and Maximum Value of a Binary Tree Finding the Sum of All Paths in a Binary Tree Time Complexity of Selection Sort in Data Structure How to get Better in Data Structures and Algorithms Binary Tree Leaf Nodes Classification of Data Structure Difference between Static and Dynamic Data Structure Find the Union and Intersection of the Binary Search Tree Find the Vertical Next in a Binary Tree Finding a Deadlock in a Binary Search Tree Finding all Node of k Distance in a Binary Tree Finding Diagonal Sum in a Binary Tree Finding Diagonal Traversal of The Binary Tree Finding In-Order Successor Binary Tree Finding the gcd of Each Sibling of the Binary Tree Greedy Algorithm in Data Structure How to Calculate Space Complexity in Data Structure How to find missing numbers in an Array Kth Ancestor Node of Binary Tree Minimum Depth Binary Tree Mirror Binary Tree in Data Structure Red-Black Tree Insertion Binary Tree to Mirror Image in Data Structure Calculating the Height of a Binary Search Tree in Data Structure Characteristics of Binary Tree in Data Structure Create a Complete Binary Tree from its Linked List Field in Tree Data Structure Find a Specified Element in a binary Search Tree Find Descendant in Tree Data Structure Find Siblings in a Binary Tree Given as an Array Find the Height of a Node in a Binary Tree Find the Second-Largest Element in a Binary Tree Find the Successor Predecessor of a Binary Search Tree Forest of a Tree in Data Structure In Order Traversal of Threaded Binary Tree Introduction to Huffman Coding Limitations of a Binary Search Tree Link State Routing Algorithm in Data Structure Map Reduce Algorithm for Binary Search Tree in Data Structure Non-Binary Tree in Data Structure Quadratic Probing Example in Hashing Scope and Lifetime of Variables in Data Structure Separate Chaining in Data Structure What is Dynamic Data Structure Separate Chaining vs Open Addressing Time and Space Complexity of Linear Data Structures Abstract Data Types in Data Structures Binary Tree to Single Linked List Count the Number of Nodes in the Binary Tree Count Total No. of Ancestors in a Binary Search Tree Elements of Dynamic Programming in Data Structures Find cost of tree with prims algorithm in data structures Find Preorder Successor in a Threaded Binary Tree Find Prime Nodes Sum Count in Non-Binary Tree Find the Right Sibling of a Binary Tree with Parent Pointers Find the Width of the Binary Search Tree Forest trees in Data Structures Free Tree in Data Structures Frequently asked questions in Tree Data Structures Infix, Postfix and Prefix Conversion Time Complexity of Fibonacci Series What is Weighted Graph in Data Structure What is the Advantage of Linear Search?

How to Start Learning DSA

All programmer experiences a point along the way where they wish they could approach a problem in a more effective manner. They finally learn about the terminology DSA while trying to find a solution to this. Before moving on to the topic of how to begin learning DSA, let's first define the terminology.

Full Form of DSA:

 Data Structures and Algorithms is the full form of DSA. It combines Data Structure and Algorithms, as the term indicates, two distinct but connected subjects.

Almost any programmer has a significant obstacle when attempting to learn DSA and become proficient in it: "How to start studying DSA?" We'll be concentrating on everything DSA in this tutorial, including how to start our DSA journey from scratch.

What exactly is data structure?

For our devices to process the data effectively and efficiently, a data structure is a specific technique to store and organise the data. To reduce time and space complexity, data structures are primarily used. The memory space and processing time needed by a convenient data structure are both modest.

Algorithm: What is it?

An algorithm is a method or a set of precise instructions that is frequently used to solve a certain number of problems or carry out a particular kind of calculation. It is a series of actions carried out sequentially to carry out a task, to put it simply.

Why should we learn about algorithms and data structures?

Let's first look at why we should learn data structures and algorithms before going on to the subject of "How to Start Learning DSA."

Each and every software application, online service, and even internet search engines use one or more data structures or algorithms to execute. So learning about data structures and algorithms is a must for understanding how any automated software or service operates.

  • Write code that is efficient and scalable - Once you are familiar with the various data structures and algorithms, you can decide which one to use when given a variety of circumstances.
  • Utilizing time and memory efficiently - Knowing about data structures and algorithms will help you create program that run more quickly and use less memory.
  • Better career chances - Data structures and algorithm questions are regularly asked in hiring processes at a range of companies, including Google, Facebook, and others.

Although these actions appear to be extremely simple, they must be carried out consistently in order to produce the intended results. According to the definition given above, an algorithm is any set of instructions for doing a certain task. The glass may therefore be differentiated as a data structure to store water and the aforementioned processes can be distinguished as an algorithm. As a result, data structures and algorithms permeate every aspect of daily life, making their comprehension essential for carrying out life's essential duties.

Where should I start?

You must first be an expert in a programming language if you are a newbie, considering starting with data structures and algorithms. Algorithms and data structures go beyond theory. The implementation of your learning in the form of programs must occur concurrently with your study of data structures. It is essential to grasp one programming language before moving on to data structures.

What coding language should I choose?

Which language to choose is likely your next worry. You must be aware that a programming language is only a means of communication with the computer in order to make this choice. Focusing on one language and mastering it is crucial.

Avoid jumping ship. Despite this, it is true that, due to its unmatched speed and widespread use, C/C++ is used by the majority of programmers in the competitive field. Therefore, if you choose C/C++, you will have access to a variety of materials to assist you.

Few steps to gaining knowledge on DSA

The most crucial issue is now, "How do I begin studying DSA?"

Individuals may follow different processes. But in this case, we're attempting to offer a broad fix for the issue. A novice can begin using DSA by following the step-by-step instructions provided below:

1. Choose a preferred programming language: Before dive into DSA, firstly we have to choose any one preferable language and learn it well from basics.

2.  Learning the concepts: Learning the concepts get a sense of the topics you need to learn about and the resources that are available before you start learning about DSA.

When learning DSA, one can start by studying the following concepts in order by dividing them into two components, such as data structure and algorithm:

  • Data Structure:
    • Array
    • Linked List
    • Stack
    • Queue
    • String
    • Matrix
    • Tree
    • Graph, etc
  • Algorithms:
    • Sorting
    • Searching
    • Hashing
    • Prefix sum
    • Suffix Sum
    • Recursion
    • Greedy
    • Backtracking
    • Dynamic Programming
    • Tree and Graph traversals
    • Advanced algorithms on tree and graph

3. Choosing the top DSA learning resources: Knowing what you wish to learn has made it easier to choose where you need to study it. There are several text-based, visual, or hybrid materials you can use to study DSA, including:

  • After researching on the internet and consulting with friends and seniors, you can buy the best book on DSA from any nearby market or e-commerce platform.
  • One can also learn through tutorials and articles that have been published online. Best technical learning websites for beginners are javaTpoint, tutorialsandexamples.
  • In addition, you can study via paid courses or free videos on YouTube.

4. Thoroughly understand the fundamentals of DSA: Now that you've gathered all the topics and materials, it's time to begin studying DSA. The most important step in learning about any algorithm or data structure is to first master the fundamentals. We cannot go to sophisticated applications or adequately apply the fundamentals without proper education.

Study each data structure's guiding principles in depth, as well as the fundamentals of algorithms using the materials you have selected. Aim to practise at each stage to assist you remember what you've learned. For this, you may also use our Practice portal.

5. Learn each data structure and algorithm individually: Once you are familiar with the basics of DSA, you should go on to learning the DSA in depth, which entails learning about the many specific applications of data structures as well as complicated algorithms and how to use them to solve issues.

To ensure that your DSA lessons are ingrained, you must practise at every stage of this process as well. To practise and assess your knowledge thus far, you can also attempt to participate in numerous coding competitions and challenges. You can check out on the internet of the Top 15 websites for coding challenges and competitions.

6. Consistency: The key is consistency; just as every monument is created brick by brick by daily, continuous labour, so is DSA. You should make an effort to understand at least 1 new topic and address 1 new problem relating to it each day. You'll be able to master DSA as quickly as possible if you make this a daily exercise.

Give coding difficulties on a consistent basis as well. At first, it could be difficult for you to solve even one difficulty, but in the end, it will all be worthwhile.

7. Avoid having a placement mindset when you are learning: We are aware that for the majority of us, studying data structures is motivated by the desire to land a job at a reputable product-based organisation. However, it's crucial to ignore this while you're learning. You shouldn't worry about things like which questions are crucial for which business, etc. Instead, you should continue to put emphasis on mastering data structures. You can only begin researching interview difficulties and experiences once you have mastered it.

8. Avoid comparison with others:  Never evaluate yourself against others. You shouldn't worry about what someone else has accomplished or how well he did it. Only you and DSA should be your priorities. Without considering whether you are behind or inferior to others, keep your attention on the subject at hand. Allocate enough time for you to learn things at your own speed.

Our ultimate goal is to become experts in DSA for beginners. Comparing yourself to others will only impede your efforts to achieve this result. So keep in mind that until you master it, it's just you, yourself, and DSA.

What is the learning curve for algorithms and data structures?

Let's start by making it clear that learning is a continuous process. Even after years of programming, there is always more to learn. However, it is reasonable to inquire "How long will it take to understand the fundamentals of DSA". A general approximation can be given for this question even if the answer varies depending on the individual and their time and dedication. If one puts in 3.5–4.5 hours a day, one may probably learn the fundamentals in 12–14 weeks. This differs from person to person once again. It may take one month for someone to master it, while others may need five to six months or longer.

Key Fundamentals to Mastering DSA

1. Spacing Repetition:

  • The most frequent query that anyone has is, "I solved a lot of questions, but even after 7–10 days, I still cannot solve them. How am I going to remember them?
  • The most important response to this is to not attempt to recall the codes. Practice and spaced repetition are the key to the solution.
  • Using spaced repetition, you may fully understand anything by going over it again and again at regular intervals until you fully understand it.

As can be observed, the ideal time intervals are to address the issue once more after a few days, once more after seven to ten days, and once more after a month. It will assist you in creating a routine for reviewing any subject at the appropriate period. It is always observed that if you review things using spaced repetition, your concepts will become more solidly developed and clear.

2. Start practicing more frequently: You are already familiar with the fundamental algorithms and data structures. The speed and efficiency with which you can identify the best course of action and reduce approach errors are now of utmost importance.

The only way to succeed in this, as with any other task, is to "practice, practice, and practice." You can practice them on a variety of online problem-solving platforms. Additionally, you are able to take part in occasionally organized real-time challenges.

3. Find solutions to various issues: It's time to expand your toolbox. After you have learned a few data structures and methods, let's move on to solving issues of various sizes and subjects.

You'll benefit from it in two ways.

  • Your pool of data structures and algorithms will grow as a result of exposure to new techniques because the challenges are not categorised. Since you've already programmed and solved so many other problems, you won't need to code them all this time.
  • You will learn about the uses of various algorithms and this will improve your ability to think.

4. Take part in coding competitions: By this point, a good deal of time has been invested, and you have also learnt a lot. However, have you succeeded in mastering DSA, the purpose of your study of data structures and algorithms?

Checking yourself out against other DSA enthusiasts and the founding fathers is the only way to find the solution. There aren't many better options than taking part in some competitive coding competitions to evaluate your skills. Your ability to code will speed up, and you'll gain a wider perspective on how a problem can have so many different methods. Additionally, it will help you become more accustomed to the interview setting because there will be time constraints in the interview.

Additionally, winning the contest is crucial for strengthening your ideas. So always solve the questions that you were unable to solve throughout the competition.

5. Evaluate some interview questions: There is always opportunity for improvement even though you have done virtually everything. Let's imagine that you consistently outperform your peers in real-time coding tasks and produce excellent outcomes. However, since competitive coding does not always call for the most effective strategy, it is probable that you are not always discovering the best solution to a problem.

There, answering interview questions may be useful. In interviews, looking for the best strategy is a natural behaviour. By being exposed to interview questions, you may now improve day by day and prepare for interviews.

How can I improve my data structure and algorithm skills?

The next objective is clearly to become proficient in DSA now that you have discovered how to begin learning it. Let's first make something clear. The ability to program well cannot be attained easily. But there is a productive way to go about it. That is dependent on things like:

  • Existing knowledge
  • The reason you desire to learn
  • Present resources
  • A wonderful manual.
  • Consistency


From the discussions above, it is transparent that, even though learning data structures and algorithms is a challenging and time-consuming process that necessitates consistent practice and hard work, if you take the proper steps and are methodical in your approach to gathering resources, making revisions, and solving practice problems, you will undoubtedly be able to learn DSA in an effective way.

One thing you must keep in mind is that learning is a constant process. Therefore, your major goal should be to continue learning DSA consistently and for at least a portion of each day. If you are inconsistent, it's likely that you will forget the material you've already studied and have to start over, which would undo all of your earlier work.

No one's struggle is perfect, but bear in mind that practise makes a man perfect. There will also be many ups and downs, so don't be afraid of them. Instead, keep learning and developing.