Hier erfahren Sie, wie Sie Algorithmen und Datenstrukturen in der Programmierung effektiv beherrschen können.

1. Choose a programming language you're comfortable with and start learning it. 2. Begin with basic data structures like Arrays, Stacks, Queues, and Linked Lists. 3. Start solving easy questions and gradually increase difficulty. 4. Once you're comfortable with basic data structures, move on to sorting algorithms and patterns like sliding window and two pointers. 5. After mastering these, move on to advanced data structures such as trees, graphs, segment trees, etc and algorithms based on these data structures. 6. Once you've covered all essential data structures, practice solving problems on advanced topics like DP on graphs and trees, segment trees, game theory, number theory, maximum flow, etc. 7. Regularly participate in live contests.

Roadmap I followed: - Learn basics (Time and Space Complexity) - Learn basic data structures (Array, stacks, queues and LL) - Pick one programming language & master its basics - Practice solving EASY problems on LC - Learn searching & sorting algorithms (For each algorithm solve atleast 2-3 problems, focus more on BINARY search) - Gradually move to advanced concepts

𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗕𝗮𝘀𝗶𝗰𝘀: Understand arrays, stacks, queues, linked lists, and their applications to build a solid foundation. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗹𝘆: Use LeetCode and HackerRank to consistently solve problems, improving your skills over time. 𝗟𝗲𝗮𝗿𝗻 𝗧𝗶𝗺𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆: Master Big O notation to analyze and optimize algorithms for efficient performance. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀: Study dynamic programming, graphs, and trees to deepen your understanding of complex algorithms. 𝗝𝗼𝗶𝗻 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻𝘀: Participate in coding contests to apply your skills and learn from others under competitive pressure.

Some was to get started are: - learn basic coding skill (learn any one language Java or c++) and understand about time and space complexity - start by solving problem solving section in Hackerrank then start participating on Contests at codechef and code forces don't get demotivated if you are struggling that means you are learning - for particular placement solve questions on leetcode as much as possible just don't solve easy ones solve all types easy medium and hard focus more on medium ones - last, how many problems you solved doesn't matter, how many quality questions you solve that matters.

To start with DSA 1. Start with a roadmap. 2. Divide DSA roadmap into two parts: a) Linear DS b) Non-linear DS 2. For first 1-2 weeks learn only the theoretical concepts and understand these 3 things for each topic : a) what is array/trees (DS) ? b) why we need it? c) real life use cases (applications) 3. Start working with the easy question and the basic search & sorting algorithms. 4. Start working on the Time & Space Complexity of these easy topics and have a handy cheatsheet. 5. Solve around 10-20 easy questions. Solve around 20-30 medium questions. Solve around 5-15 hard questions.

1. Once you understood what DSA is, the next thing you should do is, learn the concept and start practice. 2. If you just remember the questions, it won’t help. Remember, the end goal is to improve our logical thinking, not to just crack any interview. 3. Learn the concepts from youtube (or any course) and try to implement the learning on platform like Leetcode. 4. Remember, a man who practice 1 problem 10 times is better than a man who practice 10 problem just once.

Focus on understanding the fundamental principles of programming, such as algorithms, data structures, programming logic, and software engineering concepts. These fundamentals apply to any language or technology. Develop strong problem-solving and logical thinking skills. Be open to learning new languages and technologies as needed, as the ability to adapt and learn quickly is more valuable than deep knowledge in a single language. Stay updated by reading blogs, attending conferences, and taking online courses to stay informed about new tools, languages, and practices in the field of programming.

Practicing coding is essential for mastering programming, especially in algorithms and data structures. It involves understanding problems, developing logical solutions, and continuously improving one's skills through consistent effort. Here’s a comprehensive guide on how to practice coding effectively. 1. Choose a Platform 2. Start with Basics 3. Set Clear Goals 4. Structured Practice Routine 5. Learn to Debug 6. Solve Diverse Problems 7. Time Yourself 8. Review and Learn from Solutions 9. Join Coding Communities 10. Participate in Contests 11. Build Projects 12. Regularly Review and Reflect 13. Seek Feedback

Practice is what makes the most difference. I used to get confused earlier when to switch to a new topic or when to consider that one topic is complete. Later I followed the strategy to solve some easy, then a lot medium questions for that topic and when I get comfortable in medium level, I started learning the new topic while practicing some hard problems from the previous topic.

The Only best way to have good hands on DSA is to practice. The more you practice, more confident you will become. Take part in codechef, codeforces, leetcode contest, do solve as many problems you can but not similar ones. It’s not guaranteed that you will become the grandmaster or something like that but it will surely helps you to have confidence and may be one day you will become the grandmaster.

You can master Data structures and algorithms by starting from basic steps. Take baby steps to the advance level. Play with the algorithms by trying different ways. Group discussion can also be a good option where every group member shares his/her concept

Studying others feels like why I should?If it is Yes, then you are losing one of the best in depth knowledge. If it is no, then you should be open minded and allow others to review your code and ask for improvements, by reviewing others code we can learn many new features of a Framework or Language and ways of how to do a single work in many ways. PEER programming is one of the best to do code reviews. Sometimes just by viewing the code is not enough to understand the functionality, so debugging step by step by executing the code gives you briefer like how it works. One thing, this all comes with a COST OF TIME, for junior or mid-level developers time is not allotted in Agile planning, so you need to spend extra time to make it success

Analyzing code written by others is indeed a highly effective learning method, providing multiple benefits. By reviewing well-written code, you can gain insights into efficient data structure usage and elegant algorithm implementations. Participating in code reviews offers a platform to observe how experienced programmers approach and optimize their code, highlighting best practices and common pitfalls. It's crucial not to limit yourself to just reading the code. Running it, debugging it, and experimenting with different modifications can deepen your understanding and reveal how each part functions. This hands-on experience is invaluable, as it often uncovers new and more efficient ways to tackle your own coding challenges.

Studying others' code is a valuable practice for mastering algorithms and data structures. By examining how experienced programmers solve problems, you gain insights into different approaches, optimizations, and coding styles. Analyze well-written solutions to understand their strategies and techniques, and learn from their problem-solving methods. Platforms like GitHub and online coding communities provide access to a variety of solutions and discussions. Reviewing others' work helps you identify best practices, improve your own coding skills, and broaden your understanding of complex problems.

Studying others code is important because: - Some coding platforms accept the response and the person thinks he did it but there might be another solution available with efficient space and time complexities. - Might be that question is able to be solved with different data structures and algorithms. - It makes your mind to think one question in multiple ways which is helpful while solving competitive questions. - By seeing the question in different languages helps you to grasp about that programming language as well. - At last you don’t know what circumstances you have while in interviews or in competition so view all aspects of the solution.

Problem constraints, What is being asked in the question gives the idea most of the time which algo needs to be used in the question. I have shared a similar sheet in one of my videos as well. How you can map question constraints with algorithms.

I generally prescribe a strategy called EASE E - Explore the basic subset of algorithmic problems which should serve as the primer for algorithmic patterns discovery. A - Acclimatize with these algorithms. Try to solve/code them first by brute force and then by the optimised process. This will internalize the thought process. S - Simulate these algorithms in coding problems(contest) that you solve. You may hit that problem with the subset of algorithms you know or you may miss it. Celebrate the hits but reason out the miss. E - Expand. This is the advanced stage wherein if you mastered the above 3 steps for the subset. It's time to expand your boundaries and bring in more complex and exotic algorithmic arsenals in your knowledge base.

Understanding algorithm patterns is crucial for solving problems efficiently. Key patterns include Divide and Conquer (breaking problems into smaller parts, like merge sort), Dynamic Programming (solving overlapping subproblems, as in the knapsack problem), Greedy Algorithms (making locally optimal choices, such as in Dijkstra’s algorithm), Backtracking (exploring solutions by trying options and backing out if needed, like in Sudoku), and Sliding Window (maintaining a dynamic range to solve contiguous subarray problems). Mastering these patterns helps in applying systematic and efficient solutions to various problems.

Recognizing algorithm patterns helps in solving problems efficiently. Here’s a quick overview: Dynamic Programming: Use for optimization problems with overlapping subproblems (e.g., shortest paths, knapsack). Divide and Conquer: Apply to problems that can be split into smaller, manageable parts (e.g., merge sort, binary search). Greedy Algorithms: Suitable for problems where local choices lead to a global optimum (e.g., activity selection, Huffman coding). Backtracking: Useful for exploring all possibilities and backtracking on failures Graph Algorithms: Essential for problems involving networks and paths Practice these patterns to become skilled at choosing and implementing the right algorithm for various problems.

Recognizing algorithm patterns is crucial for efficiently solving problems. Algorithm patterns are common techniques or approaches that can be applied to a wide range of problems. Examples include the sliding window pattern for array and string problems, the two-pointer technique for sorting problems, and the divide-and-conquer approach for recursive problems. By understanding these patterns, you can quickly identify the most appropriate approach to a given problem. For instance, if a problem involves finding the longest substring or maximum subarray, recognizing that a sliding window approach could be applicable allows you to solve the problem more efficiently.

Building real-world projects is crucial for mastering programming. Starting small boosts confidence, while automating tasks or addressing personal needs accelerates learning. By focusing on projects aligned with your interests, you deepen understanding across the development lifecycle—from concept to deployment. This hands-on approach is universal, enhancing skills in planning, design, testing, and deployment. It sharpens technical abilities and fosters essential problem-solving skills for any programming challenge. Whether creating a utility or a complex application, each project reinforces theoretical knowledge with practical experience, preparing you comprehensively for diverse programming environments.

Applying your knowledge through real-world projects is an excellent way to master algorithms and data structures. Building or contributing to projects forces you to implement complex data structures and algorithms, which reinforces your learning and understanding. Projects present real-world problems where you can apply theoretical concepts, helping you see how they work in practice and understand their limitations. Working on projects allows you to evaluate and optimize performance in a real-world context, teaching you about trade-offs between different approaches. Projects often require creative solutions to unique challenges, enhancing your problem-solving skills and adaptability.

Practical application through projects is an excellent way to solidify your understanding of algorithms and data structures. Projects provide real-world scenarios where you can apply what you’ve learned. Whether it's developing a personal website, creating a game, or building a mobile app, integrating algorithms and data structures into these projects helps reinforce your knowledge. Start by building small projects that incorporate basic data structures, like a to-do list using arrays or a stack. Gradually take on more complex projects, such as a web crawler that uses graph algorithms or a recommendation system that implements tree structures.

When I was starting out as an android developer, after completing Kotlin basics, I was clueless as to how to put all the concepts together and learn the flow. That’s where my first dummy project came into the picture. I started by identifying the design architecture I want to use. That was followed by simply picking out five screens that I wanted to make and, voila! The work after this is pretty simple. Make your data, view models and views and, couple them as loosely as possible. Making a project will help you bring together all the core concepts that you have learnt and exposes you to the real world and prepares you for an actual job.

To truly master algorithms and data structures, applying your knowledge in real-world projects is essential. Whether you build your own projects or contribute to open-source initiatives, you’ll have the opportunity to implement complex algorithms and data structures in practical situations. This experience will deepen your understanding and solidify your skills. Additionally, projects help you learn to balance performance trade-offs in real-world applications, which is critical for writing efficient and optimized code. Engaging in real-world projects prepares you for practical challenges and enhances your problem-solving abilities.

Most common questions among students and working professionals who wants to switch. As far as college students are concerned its more about priorities and how you balance urgent and important things or how you invest your time. Working professionals have to leverage opportunities like weekends, after work hrs to balance prep with work.

Time management is the crucial aspect while coding. Manage your time effectively to laern and paractice new concepts of coding. Make yourself a coder who learns and makes different approaches to solve the problem. Read stories and blogs by top coders. They always helps you to reach your goal. Make a complete preparation guide while you are preparing for coding problems and topics such as DSA, Number Theory, etc. Give equal timing to read every concept and solve every problem within a specific timing so that you could be able to practice more tricky problems.

We start our coding journey with enthusiasm and unrealistic time tables. This results in crash landing of our goals and therefore, lost interest in coding. To tackle this particular issue, I’d suggest a time management. I personally have been a victim of having unrealistic timetables and that didn’t pan out so well for me. I’d suggest to dedicate an hour atleast to solving coding problems if you’re a working professional and, if you’re a student, I’d suggest atleast three hours per day. Further, you should always have small breaks in between to avoid burnouts and strains. Time management surely plays a great role in developing your interest in programming and, I believe with a little plan, you can ace it!

I've experienced the "use it or lose it" phenomenon myself. Our brains need to refresh the knowledge to keep it. Don't think it won't happen to you. I knew how to prove exp(i*theta) = cos(theta) + i * sin(theta) but I can't do it now without having to look up my notes anymore. Slow and steady do really win the race. Keep practicing/studying everyday, even if it's for 30 minutes. But I don't think you need to simulate the time pressure of interviews. I'm more afraid that simulating an interview whenever you study might hamper your learning curve. Just have a great time indulging yourself in the intellectual sin.