Every solution starts with a strategy, and an algorithm is a strategy for solving the coding problem. So programmers must learn to design an efficient algorithm and translate this “algorithm” into the correct code to get the job done. However there are many coding problems available in data structures and…

In this blog, we have discussed some popular strategies for solving problems in data structures and algorithms. Mastering and applying these strategies can help you more effectively tackle problems and crack the coding interview. An Incremental approach using Single and Nested loops One common approach to problem-solving is to build a solution incrementally using a loop. There are…

System design is an important topic for both technical interviews and real-world software development. It’s used to solve large-scale problems and is often asked about in interviews at tech companies. This blog will introduce you to the basic concepts of system design. What is System Design? System design deals with designing a system that…

All over the world, millions of programmers are looking forward to pursuing a dream career in computer science. On average, a dedicated programmer spends hundreds of hours learning data structures and algorithms and preparing for coding interviews. …

In the world of mathematics and algorithms, problem-solving is an art which follows well-defined steps. Such steps do not follow some strict rules and each individual can come up with their steps of solving the problem. But there are some guidelines which can help to solve systematically. In this direction…

Sometimes, designing algorithms requires a natural real-life thought process. When we observe our real-life activities, we find them filled with conditions (if and else), repetitions (loops), logical structures, optimizations, radomizations, counting, sorting, scheduling, trade-offs, and more. For example, when our flight is scheduled at a specific time, we take all…

There are a lot of variations available to dynamic programming problems. Sometimes, programmers face challenges in verifying whether the given problem is a dynamic programming problem or not. So understanding this idea is important because once we decide that the given problem can be solved using dynamic programming, it will…

Divide and conquer and dynamic programming are popular problem-solving approaches in data structures and algorithms. Both approaches share a similarity: they break problems into smaller subproblems and combine their solutions to solve the larger problem. But there are significant differences between divide and conquer and dynamic programming in several aspects: …

There are two ways to solve and implement dynamic programming problems: 1) The top-down approach and 2) The bottom-up approach. Both approaches perform similarly in one way: They use extra memory to store the solution to sub-problems, avoid recomputation and improve performance by a huge margin. …