Beginner's guide to Data Structures and Algorithms

In today's competitive world, what do you think sets you apart as a master coder or programmer? It is the ability to write optimised and efficient codes. And, how do you think that this is possible? It is only if you have a strong foundation in DSA, let's understand what is DSA.

What are Data Structures and Algorithms?

  • Introduction to Data Structures - It is all about how we organise, fetch, process, and store data quickly and efficiently. Data Structures are generally used in search engines, operating systems, computer graphics, machine learning, financial systems, and others.

Here is a live scenario to understand it better, when a user searches the query ‘How to tie a tie? in Google search it is no wonder that Google has millions of answers on how to tie a tie in its database. Then how does Google fetch, organise, and put the most relevant search results in front of its user so fast? To tackle this issue, they use Search and Sorting techniques which are components of data structures.

  • Algorithms are a set of instructions that are given to perform a particular task thoroughly in a structured manner. An algorithm takes a set of inputs and gives the desired output in order to solve a particular problem.

For example, when we give a task to a calculator to add two numbers, the algorithm in the backend will look something like this:

Step 1: Start the program.

Step 2: Declare three integer variables named num1, num2, and sum.

Step 3: Ask the user to input the value of num1 and store it in the variable.

Step 4: Ask the user to input the value of num2 and store it in the variable.

Step 5: Add the values of num1 and num2, and store the result in the variable sum.

Step 6: Display the value of the sum on the screen.

Step 7: End the program.

What's the relation between Data Structures and Algorithms?

Although Data Structures and Algorithms are used to perform different tasks in computer programming, they can't be isolated from each other. Data Structures are a way to organise and store information, while algorithms are a set of instructions that tell a computer what to do with that information. In other words, data structures are the "containers" for information, and algorithms are the "recipes" for manipulating that information.

Linear Data Structures:

Linear data structures are a way of organising data in sequential order.

  • Static:

    A static linear data structure is where the size is fixed at the time of creation, and cannot be modified during runtime.

    • Array

  • Dynamic:

    A dynamic linear data structure is where the size can be modified during runtime, allowing for the addition or removal of elements.

    • Linked List

    • Stack

    • Queue

Nonlinear Data Structures:

Non-linear data structures organise data where elements are not arranged sequentially and may have one or more child elements, allowing for more complex relationships and hierarchies.

  • Graph

  • Hash Tables

  • Trees

    • Binary Tree

    • Binary Search Tree

    • AVL Tree

    • B-Tree

    • B+ Tree

    • Red-Black Tree

So, why are Data Structures and Algorithms important to learn?

You must have frequently heard that companies consider candidates with a great understanding of DSA. You might have also heard that companies test candidates' DSA skills and want to hire those candidates who can solve problems logically at any given time.

Well, here's the deal - Data Structures and Algorithms are extremely important as they give you an insight into understanding the problem at a deeper level. It helps you choose the right solution to write the code efficiently and in an optimised way.

“Fact: Majority of questions asked in the technical round in top tech companies will be on DSA.”

How to learn DSA? We have got a roadmap too

  • Pickup a programming language.

    There is no particular language that is best for learning. But we have added a list of top programming languages used based on industry

    1. Web development: JavaScript, Python, PHP, Ruby, Java

    2. Mobile app development: Swift, Java, Kotlin, Objective-C, C#, React Native, Flutter

    3. Data science and analytics: Python, R, SQL

    4. Machine learning and AI: Python, R, Java, C++, Julia

    5. Game development: C++, C#, Java, Lua, Python, Swift, Objective-C

    6. DevOps: Bash, Python, Ruby, Perl

    7. Cloud computing: Python, Java, C++, Ruby, PowerShell, Bash

  • Learn about complexities

    Ensure to learn about time and space complexities. In simple terms, time complexity is a concept that is used to measure the time required to execute the code, and space complexity is used to measure the amount of space required to run the code efficiently.

    For example, one cup of noodles occupies less space and takes less time to cook than 3 cups of noodles which take more space and time.

  • Work on your problem-solving skills

    Problem-solving skills are the key to becoming a successful coder. Try to solve as many problems as possible in a day. You may fail in many attempts. But, failure is the stepping stone to success. Only when you fall then you realise you need to stand strong on your two feet.

  • Keep practising

    As the famous saying goes, 'Practice makes a man perfect', practising problems, mastering DSA concepts, and solving problems will help you become a successful coder. You can practise coding problems on platforms like Code Chef, Hackerrank, Leetcode, Codeforces and HackerEarth.

  • Keep an eye out for patterns

    Once you start solving problems regularly, you will notice a pattern where the logic behind the problems is similar for almost every problem and just depends on the use cases.

Wrapping up

In conclusion, we would like to add that DSA is the core foundation for any concept that is used in programming. Despite any technological advancements, DSA will remain the main concept behind every technology. So, to ensure you become a master of DSA, the most important thing to keep in mind is to remain consistent. Keep learning and growing.