Selecting the right data structure and algorithm is a critical aspect of problem-solving in computer science. Selecting the DS and Algo in CI or in our project use cases is difficult. The below cheat sheet aims to guide us through the process of making decisions while selecting a proper data structure and algorithm.
Understanding the characteristics of the problem and analyzing its requirements are key factors in identifying the best approach. The cheat sheet will provide you with a structured framework to match problem traits with the strengths of various data structures and algorithms.
We can gain insights from the below cheatsheet
- Recognize the problems that can be efficiently solved using arrays, linked lists, stacks, queues, trees, graphs, and more.
- Identifying when to employ search algorithms like binary search, linear search, or depth-first search (DFS) and breadth-first search (BFS).
- Knowing when to apply dynamic programming, backtracking, or greedy algorithms to tackle specific problem types.
- Distinguishing between problems suited for sliding window, two pointers, or hash table techniques.
This cheat sheet is your trusty companion, designed to simplify decision-making and empower you to make informed choices.
- Problem-solving is an iterative process; the more you practice, the sharper your skills will become.
- Familiarize yourself with different algorithm paradigms like divide and conquer, dynamic programming, greedy algorithms, and backtracking. Recognize when to apply each technique for optimal solutions.
- The world of data structures and algorithms is vast. Embrace a growth mindset and continue learning advanced concepts to improve your problem-solving skills continually.
- Stay curious, don't shy away from challenging problems, and enjoy the thrill of cracking them.
- Revisit key concepts, practice coding problems, and gain confidence in interviews.
This is a personal blog. The views and opinions expressed here are only those of the author and do not represent those of any organization or any individual with whom the author may be associated, professionally or personally.