Complete Data Structures and Algorithms Roadmap for Placements
Today we live in a world where data is possibly one of the most precious intangible resources out there. With such large amounts of data, it becomes imperative to derive efficient ways to organise it so that we save on time and memory. Moreover, product-based companies like Facebook and Google, which have users spread worldwide, have astronomical amounts of data that needs to be operated on in the most optimal manner possible.
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.
Let us see what Parikh Jain, Founding Member at Coding Ninjas has to say on Complete Data Structures and Algorithms Roadmap for Placements.
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 CodeStudio page for you to understand and practice.
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:
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.
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.
For a guided problem bank on Sorting Algorithms, click here.
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:
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’.
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.
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:
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.
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
Apart from the major topics discussed above, there are some more concepts that you should master in order to get good at data structures and algorithms. These are given below.
- Bit Manipulation
- Circular Queues
- Deques - Hot Topic
- Doubly And Circular LL
- String Algorithms like KMP and Z Algorithm
Frequently Asked Questions
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 you 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 an average but this being said, please note that this topic is something that you’ve got to spend time on to master. It varies from person to person how much time you’d need.
Can I learn data structures and algorithms in three months?
It is important to spend time on this topic in order to master it. Thus, setting a standard time period after which any person can be a master of it, is not viable.
Can I master data structures and algorithms in one month?
Since data structures and algorithms are something you can master only if you spend time with it, it is impossible to master them in one month unless you have previous experience that will assist you.
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 is one of the most important topics.