Before deep diving into the world of DSA, let’s first know what DSA stands for - “DSA aka DATA STRUCTURES AND ALGORITHMS”- which is a crucial step or subject in computer science degrees. To be more precise people often say without mastering DSA you can’t find a good job // myth 1.
Why DSA?
Think of DSA as the fundamental building blocks of software engineering just like bricks by which any infrastructure is built — similar to the one in which you’re reading this blog. Just as an architect needs to understand structural principles to design buildings, developers need to grasp DSA to write efficient and effective code. It's not just about passing technical interviews(read again) – though that's certainly a benefit. Understanding DSA helps you make informed decisions about which tools to use and when to use them.
Myth 1: We need to study or practice DSA just to get a job. Although you can, you shouldn’t because that will not help you in a sustainable future.
How DSA?
Some of you might say that DSA is a good practice, but how can we master it, and why do companies like FAANG consider it a crucial entry test? Let’s deep-dive into these crucial questions. First and foremost, why is DSA the most popular parameter for getting into FAANG? This is because people applying for these companies are more than the openings, so to filter out the serious and competent talents, companies make DSA a filtering round.
Myth 2: We only need to practice DSA to get into big companies or startups. In that case, my friend you are thinking wrong although DSA is a filtering round, the concepts and problem-solving it teaches you that is immersing. Practice and understand the core principles well, Don’t cramp them to get an initial job.
How DSA?
Solely only doing DSA doesn’t guarantee a job nowadays, implementing the concepts through building projects, participating in hackathons and learning in public — a personal brand does. (What is learning in the public or personal brand we’ll discuss this in the next blog - stay tuned)
Myth 3: DSA ≠Job but DSA + its implementation and projects(real-time not the dummy ones) do guarantee a job to an extent. REMEMBER
Essential Data Structures You Should Know- remember every data structure has pros and cons but the right usage of them at the right place makes them favourable.
Arrays and Lists
Arrays are the simplest yet most versatile data structures, just like your favourite actor. They offer constant-time access but can be inefficient for insertions and deletions. Dynamic arrays (like Python's lists or Java's ArrayList) automatically handle resizing but may have occasional performance hiccups during expansion.
Hash Tables
These powerful structures offer an average O(1) lookup time and are behind many real-world applications. Your favourite programming language probably implements them as dictionaries or maps. They're invaluable for caching, counting frequencies, or maintaining key-value relationships.
Trees
Do you remember your family tree — roots aka grandparents, branches aka parents and fruits and flowers aka us? Similarly, in coding, we have Binary search trees, AVL trees, and Red-Black trees that bring order to chaos. They maintain data in a hierarchical structure that enables efficient searching, insertion, and deletion.
Algorithms: The Problem-Solving Brahmastra
Sorting(just similar to sorting the best restaurants in food delivery apps)
While you might rarely implement sorting algorithms from scratch, understanding them teaches important concepts:
QuickSort demonstrates a divide-and-conquer strategy
MergeSort shows us how to break down problems into manageable chunks
BubbleSort illustrates why some approaches, though intuitive, might not be optimal
Searching
Binary search is a classic example of how clever algorithms can dramatically improve performance. Instead of checking every element (linear search), binary search eliminates half the possibilities with each step. This turns O(n) operations into O(log n).
Tips for Learning DSA
Start with fundamentals and build up gradually. Master arrays before diving into red-black trees.
Practice implementation in your preferred language (may that be C, C++, JAVA, PYTHON etc.). Understanding concepts is important, but coding them helps solidify your knowledge.
Focus on understanding time and space complexity. Big O notation isn't just theoretical – it helps you make practical decisions about which solutions to use.
Look for DSA in everyday code. Try to identify where and why certain data structures are used in libraries and frameworks you already use.
Practice . Hustle . Consistent
Remember, becoming proficient in DSA is a journey, not a destination. The goal isn't to memorize solutions but to develop a problem-solving mindset. Each concept you learn adds another tool to your programming toolkit, making you better equipped to tackle complex challenges.
Whether you're preparing for interviews or aiming to write more efficient code, investing time in understanding DSA will pay dividends throughout your programming career. The key is consistent practice and applying these concepts to real-world problems.
Happy coding FOLKS!