How I Mastered Data Structures and Algorithms

Summary

In this video, Ashish Pratap Singh shares insights on efficiently mastering Data Structures and Algorithms (DSA), which helped him clear interviews at top tech companies like Amazon and Google. He stressed avoiding time-wasting practices and focusing on key DSA topics like linear and nonlinear data structures, critical algorithms, and problem-solving techniques. The video highlights the importance of a structured learning path, understanding time-space complexity, applying practical problem-solving, and utilizing useful resources. Singh also emphasizes retention through repetition and patterns while encouraging consistent practice and resourcefulness in learning.

Highlights

• Mastering DSA helped me clear interviews at Amazon and Google. 🎯
• Avoid wasting time on ineffective learning strategies. ⏰
• Start with linear data structures then move to complex ones. 📈
• Grasp algorithms and problem-solving techniques for interviews. 💪
• Use practical learning, such as pen-paper visualization, for better retention. ✍️

Key Takeaways

• Focus on key DSA topics and structured learning paths. 🚀
• Understand the difference between linear and nonlinear data structures. 🔍
• Prioritize problem-solving over just theory. 🧠
• Utilize online resources and playlists for deeper understanding. 📚
• Practice regularly to transition knowledge from short-term to long-term memory. 💡

Overview

Ashish opens by discussing his journey in mastering Data Structures and Algorithms, noting how pivotal it was for landing jobs at major tech companies. He shares that while he initially spent excessive time on less effective strategies, he eventually honed in on a structured learning approach that led to success.

The video dives into specific DSA topics essential for tech interviews, explaining the importance of mastering linear structures first. Algorithms and problem-solving techniques are also highlighted as key areas of focus, with practical strategies like pen-and-paper visualization recommended for better understanding.

Ashish stresses the significance of consistent practice, recommending the use of online resources for solvable examples and problem sets. He emphasizes retention through repetition, pattern recognition, and advises leveraging tools like GitHub repositories for organized learning.

Chapters

• 00:00 - 00:30: Introduction and Overview The introduction chapter discusses the speaker's personal journey in mastering data structures and algorithms (DSA), which was pivotal in securing jobs at major tech companies like Amazon, Google, and Microsoft. The speaker acknowledges the challenges faced while learning DSA and reflects on the inefficiencies encountered, such as time wasted on unhelpful resources. The chapter sets the stage for the video by proposing to share effective strategies and tips that assisted the speaker in mastering DSA more efficiently, highlighting the key topics relevant for interviews and the resources deemed valuable.
• 00:30 - 01:00: Understanding DSA Topics and Pillars This chapter focuses on the strategies for mastering Data Structures and Algorithms (DSA) topics from scratch. It also provides a revision strategy to ensure that previously solved problems are not forgotten. The chapter identifies essential DSA topics for interviews and categorizes them into three main pillars: data structures, algorithms, and problem-solving techniques. It further explains that data structures can be categorized into two types: linear and nonlinear. Linear data structures are arrays, linked lists, stacks, queues, and hash tables, whereas nonlinear data structures include trees and binary search trees (SE).
• 01:00 - 02:00: Linear vs Nonlinear Data Structures The chapter titled 'Linear vs Nonlinear Data Structures' covers the progression from linear to nonlinear data structures, emphasizing the increased complexity in the latter. It touches upon various linear data structures (implied) before delving into nonlinear structures such as heaps, graphs, and Union-Find. In terms of algorithms, the chapter discusses sorting techniques, binary search, bit manipulation, and tree traversal methods including in-order, pre-order, post-order, and level-order. Furthermore, graph algorithms like DFS (Depth First Search) and BFS (Breadth First Search), as well as topological sort, and shortest path algorithms like Dijkstra's and Bellman-Ford are explored. Finally, problem-solving techniques such as two pointers, sliding window, and prefix sum are highlighted.
• 02:00 - 02:30: Algorithms and Problem-Solving Techniques Overview This chapter provides an overview of various algorithms and problem-solving techniques crucial for mastering Data Structures and Algorithms (DSA). It mentions fast and slow pointers, divide and conquer, greedy algorithms, recursion, backtracking, dynamic programming, and topk elements as important topics to focus on. While segment trees and Fenwick trees are noted, they are considered less crucial for coding interviews, as they are rarely asked. The recommendation is to prioritize understanding the more commonly asked topics. Additionally, the chapter emphasizes the importance of effectively measuring the time and space complexity of algorithms on the journey to mastering DSA. The author also reflects on a personal mistake that cost them time, implying the importance of focusing on what's most frequently tested.
• 02:30 - 03:00: Focus on Important Topics and Techniques The chapter discusses the importance of focusing on one topic at a time when learning, rather than trying to learn many topics simultaneously. This approach helps reduce stress and makes the learning process more manageable. It's essential not to aim for mastery before moving to the next topic, but to have a solid understanding sufficient to solve basic problems, such as those found on lead code. Another important aspect highlighted is understanding the proper order of learning topics, as some topics are foundational and necessary for mastering others.
• 03:00 - 04:00: Step-by-step Learning Strategy The chapter titled 'Step-by-step Learning Strategy' outlines a recommended approach to mastering recursion and solving tree-related problems. It suggests beginning with linear data structures such as arrays and linked lists, and progressing to more advanced structures like stacks and hash tables. Following this, one should learn popular algorithms and problem-solving patterns including two pointers, sliding window techniques, recursion, backtracking, sorting, searching, and bit manipulation. Next, it advises studying hierarchical data structures like trees, binary search trees, tries, and heaps. With this foundation, one can then tackle more complex algorithms such as those involving greedy methods and dynamic programming. Finally, the chapter suggests the study of graphs and union-find data structures. This structured learning strategy is designed to build a strong conceptual framework for tackling advanced computer science problems.
• 04:00 - 05:00: Order of Learning DSA Topics The chapter titled 'Order of Learning DSA Topics' begins with recommendations for beginning a new topic in data structures and algorithms (DSA). It suggests starting with the basics by understanding the topic's representation in code, the operations that can be performed, and their time and space complexities. For instance, if one is learning about binary search trees, it's crucial to comprehend its representation and learn how to perform operations like insertion, deletion, and searching for nodes. The chapter appears to guide learners on how to effectively tackle new topics in DSA.
• 05:00 - 06:00: How to Approach a New Topic The chapter titled 'How to Approach a New Topic' discusses the practical applications of algorithms and data structures beyond coding interviews. It provides the example of using Google Maps, which implements graph data structures and shortest path algorithms, to emphasize the real-world relevance of these concepts. The chapter encourages learners to explore how data structures and algorithms are applied in daily life to enhance understanding and maintain motivation. It also suggests using resources like Google search or AI tools to further investigate these applications.
• 06:00 - 07:00: Implementing and Practicing DSA The chapter 'Implementing and Practicing DSA' emphasizes the importance of understanding data structures and algorithms (DSA) by visualizing them using pen and paper. It advocates for drawing diagrams and writing pseudo-code as a means to better grasp and memorize these concepts. The chapter suggests that, for example, when learning about linked lists, drawing nodes and pointers can illustrate how nodes are connected and how operations like insertion and deletion are executed. The chapter also advises implementing DSA from scratch in one's favorite programming language after gaining a conceptual understanding, as a way to reinforce the knowledge and practice coding skills.
• 07:00 - 08:00: Resources for Learning DSA To effectively learn Data Structures and Algorithms (DSA), it's recommended to try implementing them yourself as it deepens your understanding of their mechanics. For example, when learning about Stacks, try implementing them using arrays or linked lists and develop methods like push, pop, and peek operations. While mastering implementation is great, be aware that in interviews you're more likely to use pre-existing data structures than create them from scratch. Thus, understanding built-in support for data structures and algorithms in your programming language is crucial. For instance, Python offers lists, sets, and dictionaries, and Java provides arrays.
• 08:00 - 09:00: Solving Problems over Theory The chapter titled "Solving Problems over Theory" emphasizes the importance of applying theoretical knowledge to practical coding challenges. It highlights that merely reading about a topic or watching tutorials is insufficient for mastering a programming concept. Instead, utilizing built-in libraries, such as collections with their list, map, and preset functions, is crucial. The chapter advocates for practicing coding problems from related topics after understanding their theoretical aspects. This practical application through solving around four to five easy problems helps reinforce learning and solidify comprehension.
• 09:00 - 10:00: Developing Problem-Solving Skills and Patterns The chapter titled 'Developing Problem-Solving Skills and Patterns' discusses the importance of building a strong foundation in Data Structures and Algorithms (DSA). It highlights that there are numerous quality resources available for learning DSA, particularly on YouTube, such as Abdul Bar's algorithm playlist, William F's data structure and graphs playlist, and Dynamic Programming playlists. Moreover, it mentions two algorithms courses on a platform referred to as 'corer,' and a GitHub repository titled 'awesome lead code resources' containing more valuable resources. The transcript suggests these resources are essential for mastering DSA.
• 10:00 - 11:00: Retention and Revision Strategies The chapter focuses on retention and revision strategies for understanding data structures and algorithms deeply. It emphasizes the importance of problem-solving over theoretical learning. While theory is essential at the initial stages, the chapter acknowledges that real learning solidifies through problem-solving practices. It suggests engaging with platforms like LeetCode to apply concepts practically and enhance understanding, rather than merely watching tutorials.
• 11:00 - 12:00: Consistency and Continued Learning The chapter emphasizes the importance of consistent practice and gradual progression in learning Data Structures and Algorithms (DSA). It draws a parallel between improving DSA skills and getting stronger at the gym, suggesting that just as one needs to lift heavier weights to become stronger, one should tackle more challenging problems to improve in DSA. The chapter also stresses understanding the underlying concepts and principles of DSA rather than just memorizing code or solutions.

How I Mastered Data Structures and Algorithms Transcription

• 00:00 - 00:30 getting good at data structures and algorithms helped me clear interviews at Amazon Google and Microsoft but mastering DSA was not an easy task I tried everything from books courses both free and paid ones and spent thousands of hours solving need code problems looking back I realized I wasted a lot of time on things that didn't help me get better at DSA at all in this video I will sh tips that worked for me and that can help you master DSA more efficiently without feeling overwhelmed I will talk about the important DSA topics for interviews resources I found useful and
• 00:30 - 01:00 how to master a DSA topic from scratch I will also share a revision strategy so that you don't forget the problems you have already solved what are the must know DSA topics for interviews we can group DSA topics into three pillars data structures algorithms and problem solving techniques data structures are of two types linear and nonlinear linear data structures include arrays link list Stacks cues has tables and nonlinear data structures include trees binary SE
• 01:00 - 01:30 heaps graphs try and Union find you should start with linear data structures before moving on to nonlinear ones since they are more complex for algorithms focus on salting binary search bit manipulation tree traversal algorithms including in order pre-order post order and level order graph algorithms including DFS and BFS topological salt and salt as path algorithms like Dietra and bman for and lastly for problem solving techniques you should focus on 2. pointers sliding window prefix sum
• 01:30 - 02:00 fast and slow pointers divide and conquer greedy algorithms recursion backtracking dynamic programming and topk elements there are other topics like segment trees and fic trees but they are rarely asked in coding interviews it's good to know about them but you don't need to study them deeply in the beginning focus on the most commonly asked topics also having a good understanding of how to measure time and space complexity of algorithms will be very helpful in your journey to master DSA one mistake I made that wasted a lot
• 02:00 - 02:30 of my time was trying to learn many topics at once learning many topics at the same time can become overwhelming so at the start focus on one Topic at a time it makes learning easier and less stressful we don't need to fully Master a topic before moving on to the next one but at least get to a point where you understand the ins and outs of that topic how to implement it from scratch and able to solve lead code easy problems related to that topic what's the right order to learn DSA topics the important thing to remember is that some topics depend on others for example if
• 02:30 - 03:00 you don't understand recursion you will struggle to solve tree related problems here is the order I recommend start with linear data structures like arrays link places stacks skes and has tables then learn popular algorithms and problem solving patterns like two pointers sliding window recursion backtracking salting searching and bit manipulation next study hierarchical data structures like trees binary sear trees try and heaps after that learn more complex algorithms like greedy and dynamic programming finally study graphs and Union find data structures this is just
• 03:00 - 03:30 my recommendation but you can change it based on what you already know and what you prefer to learn how to start learning a new topic you can follow these steps to get a head start on a new topic first start with Basics Begin by learning what it is how it's represented in code different operations you can perform on it and the time and space complexities for example if you're learning B sores understand how it's represented and learn common operations like inserting a node deleting a node and searching for a node second learn real applications data sectes and
• 03:30 - 04:00 algorithms are not just called coding interviews they are used in real life too for example when you use Google Maps to find the sest route to your friend's house it use graph data structure and sort as path algorithms like Direct that's why when you are learning it's good to understand how these concepts are used in real life this can help you see why data structures and algorithms are important and it might make learning them more exciting you can do a Google search or ask AI tools like chib to learn how a particular data structure or algorithm is used in real life next use
• 04:00 - 04:30 pen and paper one of the best ways to understand DSA topics is to visualize them using pen and paper visualizing data structures and algorithms can help you understand and remember them better draw simple diagrams and write pseudo code to see how the data structure or algorithm works for example while learning about link list draw NES and pointers to see how notes are connected and how operations like insertion and deletions are performed next implement it from scratch once you understand how it works code it out from scratch in your Fab programming language
• 04:30 - 05:00 implementing it yourself reinforces your understanding and helps you grasp the underlying Mechanics for example when you are learning about Stacks try implementing them using arrays or link list Implement methods for push pop and Peak operations next learn init libraries in most interviews you probably won't be asked to implement a data structure from scratch but you will need to know how to use them to solve coding problems most popular programming languages come been built in support for popular data structures and algorithms for example python has list sets and dictionaries and Java provides array
• 05:00 - 05:30 list has map and preset as part of the collections Library so learn how to use these built-in libraries and the various methods each data structure offers and the last solve easy problems just reading about a topic or watching tutorials is not enough you need to practice coding problems related to the topic to build confidence and reinforce learning this practice helps you apply the concept you have just learned and solidifies your understanding after understanding the basics of a topic and it implementation try solving four to five easy problems on code related to
• 05:30 - 06:00 the topic you're currently learning what are some good resources to learn DSA there are many great resources available online especially on YouTube here are some resources that I personally found quite useful to build a solid foundation in DSA Abdul Bar's algorithm playlist William F data structure and graphs playlist to S dynamic programming playlist and these two courses on corer aard algorithms one and algorithms 2 outut these and more DSA resources in my GitHub repository called awesome lead code resources you can find the link in the description how to master and Esa
• 06:00 - 06:30 topic after you have learned the basics you are ready to go deep here are few tips to understand data structures and algorithms in depth number one prioritize solving problems over Theory while understanding the theory is important in the beginning the real learning happens when you apply that knowledge to solve problems the more problems you solve the better you will understand data structures and algorithms so don't spend all your time watching tutorials once you understand a concept you start solving problems related to it on lead code if you want to learn about how to start lead code and use it effectively you can watch
• 06:30 - 07:00 this video next challenge yourself getting good at DSA is like getting stronger at the gym lifting the same bits every day won't make you stronger you need to slowly lift heavier weights to we muscles similarly to improve a DSA you should gradually go beyond your comfort zone and Tackle harder problems for example if you can comfortably solve most easy problems move on to medium problems number three understand don't memorize when learning DSA focus on understanding the underlying Concepts and principles instead of just memorizing code or Solutions memorizing
• 07:00 - 07:30 may help you solve specific problems but it limits your ability to apply your knowledge to new scenarios it's okay if you need to look at the solution for a difficult problem but try to understand why the solution works and then implement it by yourself if you're having trouble understanding the answer try drawing it out with a pen and a paper usually for challenging problem there is a specific trick or coding pattern knowing which makes the solution easier so for every hard problem ask yourself what is that one thing knowing which made everything else easier next think in patterns one of the fastest
• 07:30 - 08:00 ways to get better at DSA is to think in terms of patterns knowing these patterns can help you quickly identify the right approach to solve new problems and enable you to solve a large number of problems in lesser time as you solve more problems notice the techniques and approaches that come up often group similar problems together and find the common strategies used some of the common problem solving patterns are two pointers used to solve problems involving arrays or link list especially where you need to find pairs or triplets that satisfy certain conditions sliding
• 08:00 - 08:30 window used for problems involving Ser arays or server strings particularly when you need to find the maximum minimum or a specific condition within a fix size window fast and slow pointers use for problems related to finding Cycles in link list or arrays Marg intervals use for problems involving patterns such as merging overlapping intervals and backtracking which is used for problems involving permutations combinations and other scenarios where you need to explore all possible configurations you can find more patterns in this GitHub repository called awesome lead code resources if you want me to make detailed videos on
• 08:30 - 09:00 these patterns let me know in the comments and make sure to subscribe so that you don't miss my new videos how to retain what you have learned mastering DSA requires not only learning new Concepts and solving problems but also retaining that knowledge over time how the problems that I couldn't solve in one go and I had to look at the solution I realized that I soon forgot how to solve those problems after a few weeks here are some tips to help you remember what you have learned number one repetition when we learn something new it goes into our solter memory but when we repeat it many times it moves to a
• 09:00 - 09:30 long-term memory therefore repetition is important to transfer Knowledge from short-term to long-term memory by revising Concepts and problems regularly you reinforce your understanding and make it easier to recall the information later so regularly visit problems you found challenging and try to solve them again without looking at Solutions here is an effective approach I followed for the problems I couldn't solve in the first attempt on lead code you can create list make tool is revision one and revision two if you can't solve a problem on your first attempt and need to look at the solution put it in the revision one list after a few weeks try
• 09:30 - 10:00 to solve problems in the revision one list if you can solve them on your own move them to the revision two list if not leave it on the revision one list a few weeks later try to solve the problems in the revision two list if you can solve it by yourself well done youve got it remove it from the list and next bookmark everything important having quick access to high quality resources saves time and helps you revisit important Concepts and solutions so use browser bookmarks or tools like Google Drive notion to save links to useful articles tutorial and problem solving guide you can also tag and organize
• 10:00 - 10:30 bookmarks by category for easy access you can find my most important resources in the giup repository I mentioned earlier and finally be consistent learning DSA takes time and the only way to improve is to keep working at it some topics might take weeks or months to Masters so be patient with yourself and it's normal to feel stuck or frustrated when solving a challenging problem or trying to understand a complex topic just keep going if a problem seems too hard take a break and then try again set a goal based on how much time you want to de to your preparation and practice
• 10:30 - 11:00 regularly even if it's just one or two problems every day if you enjoyed this video you will also like my newsletter where I write articles on important topics related to coding DSA and system design you can subscribe it at blog. algom master.