Learning algorithms not helping you ace interviews? Discover the hidden framework behind all LeetCode questions that unlocks the secrets to solving problems methodically and efficiently.As an aspiring software engineer prepping for technical interviews, I often hear the same advice: “Practice on LeetCode!” LeetCode has become the go-to platform for solving coding challenges to build core programming and algorithm skills. Here’s how I’ve been able to start successfully tackling LeetCode as a beginner:
Pick One Language LeetCode supports many languages, but it’s best to stick with just one. This allows you to standardize your process instead of switching syntaxes. As a Python developer, I opted to solve all challenges in Python for consistency.Review the Topic Tags LeetCode organizes challenges by topic like arrays, strings, dynamic programming etc. Identify your weaker areas and focus practice there. For me, it was essential algorithms like sorting/searching.
Start with Easier Difficulty Problems Don’t dive right into the hard challenges! Build confidence by solving some straightforward problems first. Level “Easy” is a good starting point to get used to thinking algorithmically.Break Down the Problem Read the problem description, inputs, outputs carefully. Then break it down step-by-step. What are the base cases? Any gotchas? Don’t start coding right away.
Write Down Your Approach Write out the crux of your solution before coding. This could just be some notes and pseudocode. Verbalize your thought process. Imagine explaining it to someone else.Code with Intent Once clear on the approach, start writing actual code. Name variables intuitively, use indentation and spacing liberally, include comments explaining the logic.
Test Thoroughly Devise sample inputs to test your code. Consider edge cases. Does it work correctly on all inputs? Are there opportunities to optimize efficiency?Review Solutions After submitting your own solution, review solutions others have submitted. Compare approaches and see what you can learn. Don’t get discouraged if others used more optimal algorithms.
Grind and Repeat Rinse and repeat consistently. LeetCode takes patience and practice. Over time, you’ll gradually accumulate knowledge and techniques for solving coding challenges efficiently.
LeetCode has been invaluable for strengthening my core programming abilities. I encourage all aspiring engineers to incorporate it into their interview prep!Master the art of the algorithm interview. Learn how to turn recursive backtracking, dynamic programming, and other CS fundamentals from textbook concepts into solved LeetCode challenges.
Sliding Window
- Useful for problems involving a linear sequence, e.g. longest substring. Maintain window of fixed size and slide along.
Two Pointers
- Leverage two pointer variables to expand or contract range. Helps for problems on arrays, linked lists etc.
Fast and Slow pointer
- Variation on two pointers with one pointer moving faster. Useful for cycle detection.
Binary Search
- Quickly locate item in sorted arrays. Modify bounds based on condition. Great for efficient searching.
Recursion
- Many problems can be solved recursively by reducing problem size in each call. Useful for tree problems.
Backtracking
- Incrementally build solutions, abandoning partial solutions that don’t satisfy constraints. Works for subset/permutation problems.
Dynamic Programming
- Optimize recursive solutions by caching partial results. Key for optimized recursion and sub-problem reuse.
Greedy Approach
- Build up optimal solution piece-by-piece by making locally optimal choices. Applies to many optimization problems.
Bit Manipulation
- Great for problems involving bit-level operations and representations.
Prefix/Suffix Sum
- Pre-compute sums of array slices to efficiently find range sums in constant time.
There are more patterns like topological sort, union-find, monotonic stacks etc. but these form a solid foundation for developing strong problem solving skills to tackle most LeetCode questions. Recognizing related patterns helps solve problems systematically.