Searching, sorting, queues, trees, graphs, linked lists, stacks, arrays, etc.
Do you wonder why one should study all the above-mentioned complicated material if it has no real-world application? Why do businesses ask questions about data structures and algorithms if they aren't relevant to their day-to-day work?
Do you know that under the hood all your SQL and Linux commands are algorithms and data structures? You might not realize this, but that’s how the software works.
Let's have a look at what DSA basically is and why it is so important?
What are Data Structure and Algorithms?
Data structures and algorithms help in understanding the nature of the problem at a deeper level and thereby a better understanding of the world.
Data Structures and Algorithms are the foundation of every software and project we develop. DSA is used in developer interviews to measure the student's reasoning and problem-solving abilities. The amount of competitive programming websites and courses available in DSA has increased dramatically in recent years.
The algorithms and data structures give the programmer a variety of options for processing the data effectively. A programmer may not produce effective and reliable code for their software if they are unfamiliar with data structures and algorithms. It is essential for job success and a vital component of computer science.
DSA, for many undergrads, is a nightmare and is found tough to go about. The roots of how multiple things work in computers/computer science are in DSA. The importance of DSA is not only in terms of a “hot hiring skill” but as an asset that allows a better understanding of more complex things in the world of computers.
Data structures :
● Array — Stores the data with the same data type in contiguous memory locations
● Linked List — Consist of a chain of nodes containing data, which are connected via links.
● Queue — It is a data structure that follows the first-in-first-out methodology
● Stack — Linear data structure, which follows the last-in-first-out methodology
● Trees — Hierarchical data structure defined as a collection of nodes
● Graphs — Nonlinear data structure consisting of nodes and edges
Algorithms:
● Searching algorithms like linear search, binary search, and Fibonacci search.
● Sorting techniques such as merge sort, quick sort, and heap sort
● Graph and tree algorithms such as Dijkstra's algorithm, DFS, and BFS
Why Learn DSA?
• Write optimized and scalable code - Once you have knowledge about different data structures and algorithms, you can determine which data structure and algorithm to choose in various conditions.
• Effective use of time and memory - Having knowledge about data structures and algorithms will help you write codes that run faster and require less storage.
• Better job opportunities - Data structures and algorithms questions are frequently asked in job interviews of various organizations including Google, Facebook, and so on.
The major goal here is to not cram anything; instead, understand the ideas and apply them to the problems to improve your DSA knowledge. "The more you practice, the more you learn,".
TIPS TO ACCELERATE YOUR JOURNEY IN DSA
• Understand the fundamentals of the language that you are programming in. Learn beyond the theory by implementing all concepts in diverse ways.
• Understand the depth of time and space complexity. Code and test.
• Focus on strengthening logic instead of studying existing codes. A better logic will help you solve more unseen questions.
• Improve problem-solving skills not only specific to programming. It changes the way you think and can help you gain more exposure to larger problems.
• Practice coding on sites like Leetcode, Code Studio, etc. While practicing, ensure that you solve at different difficulty levels. Do not stick to a particular level and solve all from that only. This will reduce your exposure to higher-difficulty questions.
• Keep calm and believe in yourself. No question is unsolvable.
• To improve your grasp of data structures and algorithms, start by assessing what you already know and where your knowledge and understanding of data are lacking.
Here, We’ve enlisted a curated list of resources to learn DSA from zero to advanced level for placements and interview preparations.
1. VIDEO LECTURES or YOUTUBE
There are plenty of videos available on each topic for DSA. To understand the basics and implementation of each data structure, it would be great to binge-watch lecture series along with writing some code samples. Either you can by researching each topic and watching multiple teachers or you can follow a playlist/a particular teacher. Some of the best YouTube channels and playlists to learn DSA are:
• MyCodeSchool
• MIT Open Courseware
• Apna College
2. ONLINE COURSES
There are plenty of paid courses available for free enrolment during their offer period. You can also enroll in free courses with or without certificates which will upskill you with experienced trainers. The point to note is that certificates of courses are not important as they are not considered enough unless you have some achievement to showcase that skill. The best way to highlight that skill is to achieve some good rank and ace coding contests.
3. BOOKS
Books are one of the best ways to learn concepts and practice them. Since ancient times humans have had this primary source of learning it’s still impactful for one. Various books explain the programming concepts from beginner to advanced levels in an expressive manner. I’ve listed some books that are great for beginners and some for advanced learners.
• C++ Primer
• Cracking the Coding Interview
• Grokking Algorithms
• Introduction to Algorithms
• Elements of Programming Interviews in C++
• Data Structures and Algorithms in Java
4. Miscellaneous
You can participate in hackathons for beginners and learn from different blogging websites. “Knowledge is of no value unless you put it into practice”. After going through all the basic concepts and implementations, you need to practice questions to acquire the problem-solving skills, the basis for all tech interviews. Many websites provide question banks for practice.
A few tips from our side to learn DSA efficiently are:
Stop trying to memorize everything. Instead, start with the basics and learn to do two things:
• Visualize the data structure Intuitively understand what the data structure looks like, what it feels like to use it, and how it is structured both in the abstract and physically in your computer's memory. This is the single most important thing you can do, and it is useful from the simplest queues and stacks up through the most complicated self-balancing tree. Draw it, visualize it in your head, whatever you need to do: Understand the structure intuitively.
• Learn when and how to use different data structures and their algorithms in your own code. This is harder as a student, as the problem assignments you'll work through just won't impart this knowledge. That's fine. Realize you won't master data structures until you are working on a real-world problem and discover that a hash is a solution to your performance woes. But even as a student you should focus on learning not the minutia details but the practicalities: When do you want a hash? When do you want a tree? When is a min-heap the right solution?
• To begin with, preparation you need to make your own roadmap and choose only those resources which are feasible for you. Some days, it might not be possible to touch these concepts but practice daily to strengthen your learning. Solving at least 10 Questions(3-Hard,5-Medium,2-Easy) regularly will help you clear your concepts and improve your logical thinking. Do not get discouraged when you can’t solve problems, learn from your mistakes. Lastly, staying motivated throughout the learning period is essential to achieving your goal, don’t let failures stop you.
Hope this helps you along your journey.
Final Words
In general, software development requires daily technology learning. While utilizing these technologies in one of your applications, you get to learn most of them. Algorithms, on the other hand, do not work that way.
Good luck with your programming journey! It’s certainly not going to be easy, but by following these steps, you are one step closer to mastering data structure and algorithms than others.
In the end, if you are starting to get nervous seeing so much to learn, COOL DOWN. Learning CP is really a fun process and you will surely enjoy it.
Please suggest any improvements or some other useful resources you feel are worth adding.
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