**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:

**3.6 Linked Lists**

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.

- Graph Traversals - BFS And DFS
__DFS Algorithm__- MST
- Shortest Path Algorithms
- Topological Sort
- Graphs in Matrix

**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.

- DP with Arrays
- DP With Strings
- DP With Maths
- DP With Trees
- Breaking And Partition Based Problems
- Counting Based Problems

**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__