1.
Introduction
2.
What is Data Structure?
3.
5 Steps to Learn DSA from Scratch
3.1.
1. Learn a programming language of your choice
3.2.
2. Learn about Time and Space complexities
3.3.
3. Data Structures and Algorithms
3.3.1.
3.1 Arrays & Strings
3.3.2.
3.2 Multidimensional Arrays
3.3.3.
3.3 Recursion and Backtracking
3.3.4.
3.4 Sorting Algorithms
3.3.5.
3.5 Binary Search Applications
3.3.6.
3.3.7.
3.7 Stacks and Queues
3.3.8.
3.8 Binary Trees
3.3.9.
3.9 Priority Queues and Heaps
3.3.10.
3.10 Graphs
3.3.11.
3.11 Dynamic Programming
3.4.
4. Practice, Practice, and Practice more
3.5.
5. Compete and Become A Pro
4.
Why are data structures and algorithms the first step?
5.
How to decide which field I should pursue?
6.
Why Web Development?
7.
8.
8.1.
What is the fastest way to learn data structures and algorithms?
8.2.
How long does it take to complete data structures and algorithms?
8.3.
What do I need to know before starting DSA?
8.4.
What topics should I cover in DSA?
9.
Conclusion
Last Updated: Aug 6, 2024
Medium

# Complete Roadmap To Learn DSA

## Introduction

DSA, or Data Structures and Algorithms, are fundamental concepts in computer science. Data structures are ways to organize and store data, while algorithms are step-by-step procedures for solving problems. Proficiency in DSA is crucial for developing efficient software and solving complex computational challenges.

This efficient organisation of data requires programmers who are skilled in the usage of Data Structures and Algorithms. Therefore masters of this topic are always sought by big product-based companies because it ensures good handling of their data and provides a good measure of a candidate's problem-solving skills.

Therefore, for all aspiring to be excellent and in-demand programmers, it is necessary to acquire skills in Data Structures and Algorithms. Now the question that arises is that of how. How does one prepare DSA? Well, this article will lay down a complete Data structures roadmap for you to answer just this.

## What is Data Structure?

A Data structure is like a specialized container for organizing and storing data in a computer program. It's a way to structure and manage information to make it easy to access and manipulate. Think of it as different types of boxes to store things – each box is designed for specific purposes. For example, arrays are like shelves with numbered slots, while linked lists are like a chain of interconnected boxes. Choosing the right data structure is essential for efficient data management and quick retrieval, which is crucial in computer programming to build fast and effective software.

Also see, Ensemble Learning

## 5 Steps to Learn DSA from Scratch

Let’s discuss all the 5 steps in detail below:

• Learn a programming language of your choice
• Learn about Time and Space complexities
• Learn the basics of individual Data Structures and Algorithms
• Practice, Practice, and Practice more
• Compete and Become a Pro

### 1. Learn a programming language of your choice

Learning a programming language of your choice is crucial for cracking Data Structures and Algorithms (DSA) interviews. DSA is a fundamental concept in computer science, and it requires a solid understanding of programming languages to be successful. By mastering a programming language, you will not only be able to demonstrate your technical skills but also showcase your problem-solving abilities. It is recommended to choose a language that you are comfortable with and that is widely used in the industry, such as Java, Python, or C++. Once you have chosen your preferred language, it is essential to practice coding exercises and solve DSA problems. There are various online resources and coding platforms available that provide a vast collection of DSA problems and offer different levels of difficulty. By practicing regularly and improving your programming skills, you will be better prepared for DSA interviews and increase your chances of landing a job in the tech industry.

### 2. Learn about Time and Space complexities

If you're preparing for a DSA (Data Structures and Algorithms) interview, it's important to have a solid understanding of time and space complexities. Time complexity refers to how much time an algorithm takes to execute as the input size grows, while space complexity refers to how much memory an algorithm uses as the input size grows. Understanding these concepts is crucial because interviewers often ask questions that require you to analyze and optimize the time and space complexity of an algorithm. To prepare for these types of questions, you should practice analyzing the time and space complexities of common algorithms, such as sorting and searching algorithms, and be able to identify the best algorithm for a given problem based on its time and space complexity. Additionally, you should be able to explain your thought process and reasoning behind your solution to demonstrate your understanding of these concepts. By mastering time and space complexities, you'll be better equipped to ace your DSA interviews and land the job of your dreams.

### 3. Data Structures and Algorithms

When it comes to cracking DSA (Data Structures and Algorithms) interviews, it's important to have a strong understanding of the fundamentals. Data structures like arrays, linked lists, stacks, queues, trees, and graphs are essential building blocks for solving complex algorithmic problems. Understanding the time and space complexity of different operations on these data structures is crucial for optimizing your solutions. Algorithms like sorting, searching, dynamic programming, and graph algorithms are also important to master. It's important to know the different approaches for solving a problem and to be able to choose the most efficient one. In addition to technical knowledge, it's also important to practice problem-solving skills.

#### 3.1 Arrays & Strings

The first topic that we start with within the Data Structures roadmap is Arrays. An array is a collection of homogeneous elements stored in a contiguous block of memory. The size of an array is always pre-defined. For a visual representation, take a look at the following diagram.

Every element (denoted as 'Value') has a corresponding index number. For example, for an array of length 5, the index numbers increment as 0, 1, 2, 3, and 4. An array that consists of characters as elements is called a string. What makes it different from a character array is that it ends with a special symbol ' \0'.

Some essential sub-topics from Arrays and Strings that are important for placements are given below. In addition, every topic is linked to its Coding Ninjas Studio page for you to understand and practice.

#### 3.2 Multidimensional Arrays

Arrays can be one-dimensional or multi-dimensional. Multi-dimensional array concepts are applied at very important places like in matrices. A visual representation would be such:

The most important problems from this topic are Traversal Based Problems and Rotation based problems.

#### 3.3 Recursion and Backtracking

Recursion is a programming technique in which a function calls itself, directly or indirectly. It is majorly used in complex problems where we can divide the main problem into smaller subproblems.

Backtracking is a programming technique that uses recursion to build an overall solution by incrementally going through smaller levels of the problem until the solution is reached.

The topics under this category that you must master are given as follows.

#### 3.4 Sorting Algorithms

When we have such astronomical amounts of data, doing operations on it requires it to be ordered in a particular manner depending on the final usage. There are various types of sorting algorithms that are used to sort data. These are important from the perspective of studying for interviews.

Continuing the data structures roadmap, the two most important sorting algorithms are Insertion Sort and Selection Sort

#### 3.5 Binary Search Applications

The next topic in the data structures roadmap is Binary Search. Binary Search is a searching algorithm that works on the principle of divide and conquer. It works by comparing the target element to the middle element of the collection. A new collection is formed after every comparison by repeatedly dividing the main collection into halves. Important problems to practice from this topic are:

Linked List is a dynamic linear data structure that consists of nodes where each node consists of two fields – data and pointer. First, take a look at the visual representation of Linked Lists:

The first node is called the Head and consists of a pointer that points to the second node. Similarly the second node points to the third node and so on. Linked Lists are of three types - Singly Linked List, Doubly Linked List and Circular Linked List.

Some important problems from this topic are as follows:

#### 3.7 Stacks and Queues

Stack and Queue, both are linear data structures. Stacks work on the principle of LIFO (Last In First Out), whereas Queues work on LILO (Last In Last Out). A visual representation of both would make things clearer.

Above is a representation of a stack. Inserting an element into a stack is termed 'Pushing', and deletion of an element from the stack is termed 'Popping'.

Now let’s see how Queues look.

You can see from the above diagram that, a Queue has a Front and a Back. Addition of an element to the queue is done to the Back and deletion of an element from the queue is done from the Front. The former is termed as ‘Enqueuing an element’ and the latter as ‘Dequeuing an element’.

There are two sets of problems under these two topics that are important to be mastered - Implementation Based Problems and Application Based Problems. Head to the links to start practicing.

#### 3.8 Binary Trees

The next step in the data structures roadmap is the Binary Tree. A Binary tree is a type of tree data structure in which every node has at most two children. Each node consists of three fields – data, a pointer to the left child, and a pointer to the right child. Some important concepts to master from this topic are given below.

#### 3.9 Priority Queues and Heaps

A priority queue is a queue in which every value is associated with a priority. Dequeuing takes place in a manner that the element with a higher priority is dequeued before an element with a lower priority. Heaps are a special type of data structure which are based on complete binary trees.

There are three kinds of problems part of the data structures roadmap that are important to be mastered from this topic:

#### 3.10 Graphs

A Graph is a non-linear data structure that consists of nodes and edges. A node is a vertex and an edge is a line or curve that connects any two vertices. Some important concepts to be mastered in this topic are given below.

#### 3.11 Dynamic Programming

A very important topic in the data structures roadmap is Dynamic Programming. It is an optimization technique working on recursion in which we divide a complex problem into smaller subproblems. It finds many applications in computer science. Some important concepts from this topic are given below.

### 4. Practice, Practice, and Practice more

The key to getting better at any skill is practicing. You should make practicing DSA questions a part of your routine during your preparation. Most DSA websites have a problem-of-the-day feature that can help you maintain your consistency, and Coding Ninjas Studio has this feature as well.

### 5. Compete and Become A Pro

Competing in contests is the most important part of getting better at DSA. When it comes to solving questions in online assessments, time is the most important aspect. Time is the most important aspect of performing well in online assessments. You should regularly participate in coding contests on various platforms to improve your problem-solving speed.

Also read - Data Structure MCQ

## Why are data structures and algorithms the first step?

The amount of times that emphasis has been laid on the fact that data structures and algorithms are the most important topics in order to get good at programming is infinite.

But why is it so?

In one of the articles I have written on this topic, I gave this example that might make it clearer.

If you have ever ordered food online using the mobile applications of various Food Delivery companies like Zomato, you would know how you can track your order in real-time. Once you place your order, Zomato selects one employee who is allocated your order. This process that seems so simple from the outside actually uses a lot of mathematical techniques which essentially are built on the foundations of Data Structures and Algorithms.

If you try to make a basic food ordering system, you might use three kinds of data structures. You can use Linked Lists to store the items on a menu, Stacks to store the items that a user orders and Queues to store the orders from various users. The more interesting part is understanding how these structures will implement the operations we need to do and why we use these. These questions, you will be able to answer yourself once you delve into this subject.

Another application of data structures is seen in web scraping. It is a technique used to extract data from a website, which means scraping it from HTML files. HTML in itself is a binary tree. So in order to perform web scraping successfully, one must have knowledge about binary trees which are a type of data structures.

## How to decide which field I should pursue?

After you have attained mastery in data structures and algorithms the next step would be to identify what field you would like to go into professionally. But before that, it is also advisable to get yourself into competitive programming which will improve your hold on DSA and also help immensely to hone your programming skills.

But competitive programming and DSA are just gateways to what you will do in the industry. It can range from web development, android development to machine learning and artificial intelligence. But the question is how does one decide which one to choose. For this, you need to consider the following factors:

1. Identify your inclination: if you have any inclination towards any field you should not hesitate in going for it. Because if you have the passion to do something in an area that you like, it is certain that you will be able to do it. Thus, whether it is web development or machine learning, or any other field, if your wish is to go with it, you should go with it.

2. If you do not have an inclination: It is possible that you might not have yet recognized a liking for a particular field. In that case, what is advisable, is to go for web development. To understand why we discuss this in detail in the next section.

## Why Web Development?

There are a number of reasons why web development is the right path to take if you are unsure about which field to go to. Let us discuss these reasons one by one:

1. Job opportunities: It is natural that every company whether it deals in any kind of products or services needs a website/ server to operate. Even a company that works on machine learning algorithms will need a server to actually deploy those algorithms. Every firm thus needs web developers. In such a scenario it becomes evident that the job opportunities that a person with a web developing profile would get are much more than any other profile. So in case you do not know where to go, web development is the perfect field that will get you countless opportunities.

2. Foundation of everything: As we discussed in the first point, web developers are much needed by any kind of firm. This is because nothing at all can operate fully without web development being involved. This in turn leads to countless chances for web development to flourish. Keeping this in mind, we can say that there is never going to be any decrease In the demand for web developers.

## Tips to boost your learning

Start with Basics: Understand basic concepts like arrays, stacks, and queues. These are the building blocks for more complex topics.

Practice Coding: Try coding problems daily on platforms like Coding Ninjas, LeetCode, or HackerRank. Practice makes perfect!

Visualize Problems: Draw diagrams or use online tools to visualize how algorithms work. It helps in understanding better.

Learn from Mistakes: When you get stuck, look at the solution and understand where you went wrong. Learning from mistakes is key.

Group Study: Sometimes, studying with friends can help. You can learn from each other and solve problems together.

Use Online Resources: There are lots of free tutorials and videos online that can help explain complex topics in simple ways.

Build Projects: Apply what you’ve learned in small projects. It's a fun way to test your skills and learn new things.

### What is the fastest way to learn data structures and algorithms?

Data Structures is a topic that you must give time to in order to get good at it. Learning it quickly will produce no results, and the purpose of your preparing it would thus be failed.

### How long does it take to complete data structures and algorithms?

Data Structures and Algorithms would require 3-4 months on average, but this being said, please note that this topic is something that you’ve got to spend time mastering. It varies from person to person how much time you’d need.

### What do I need to know before starting DSA?

Before getting started with data structures and algorithms, you should learn at least one programming language, such as C++, Java, or Python. You should also go over the basics of time and space complexity analysis.

### What topics should I cover in DSA?

Cover key topics in Data Structures and Algorithms (DSA): arrays, linked lists, trees, sorting, searching, graphs, dynamic programming, and algorithm complexity (Big O notation).

## Conclusion

In this article, we discussed the importance of Data structures and then laid down a data structures roadmap going topic-wise and listing down the most important concepts from each topic. Mastering these concepts will give a boost to your preparation for interviews in which data structures are one of the most important topics.

To learn more about Data Structures and Algorithms, you can enroll in our DSA in C++ Course.

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