@ -112,13 +112,20 @@ If you are given a sequence and the interviewer asks for O(1) space, it might be
This might be obvious, but traversing the array twice/thrice (as long as fewer than n times) is still O(n). Sometimes traversing the array more than once can help you solve the problem while keeping the time complexity to O(n).
This might be obvious, but traversing the array twice/thrice (as long as fewer than n times) is still O(n). Sometimes traversing the array more than once can help you solve the problem while keeping the time complexity to O(n).
## Recommended questions
## Essential questions
_These are essential questions to practice if you're studying for this topic._
- [Pacific Atlantic Water Flow](https://leetcode.com/problems/pacific-atlantic-water-flow/)
- [Pacific Atlantic Water Flow](https://leetcode.com/problems/pacific-atlantic-water-flow/)
- [Number of Connected Components in an Undirected Graph (LeetCode Premium)](https://leetcode.com/problems/number-of-connected-components-in-an-undirected-graph/)
- [Number of Connected Components in an Undirected Graph (LeetCode Premium)](https://leetcode.com/problems/number-of-connected-components-in-an-undirected-graph/)
@ -60,9 +60,17 @@ If you see a top or lowest _k_ being mentioned in the question, it is usually a
If you require the top _k_ elements use a Min Heap of size _k_. Iterate through each element, pushing it into the heap (for python `heapq`, invert the value before pushing to find the max). Whenever the heap size exceeds _k_, remove the minimum element, that will guarantee that you have the _k_ largest elements.
If you require the top _k_ elements use a Min Heap of size _k_. Iterate through each element, pushing it into the heap (for python `heapq`, invert the value before pushing to find the max). Whenever the heap size exceeds _k_, remove the minimum element, that will guarantee that you have the _k_ largest elements.
## Recommended questions
## Essential questions
_These are essential questions to practice if you're studying for this topic._
- [Merge K Sorted Lists](https://leetcode.com/problems/merge-k-sorted-lists/)
- [Merge K Sorted Lists](https://leetcode.com/problems/merge-k-sorted-lists/)
- [K Closest Points to Origin](https://leetcode.com/problems/k-closest-points-to-origin/)
## Recommended practice questions
_These are recommended questions to practice after you have studied for the topic and have practiced the essential questions._
- [Top K Frequent Elements](https://leetcode.com/problems/top-k-frequent-elements/)
- [Top K Frequent Elements](https://leetcode.com/problems/top-k-frequent-elements/)
- [Find Median from Data Stream](https://leetcode.com/problems/find-median-from-data-stream/)
- [Find Median from Data Stream](https://leetcode.com/problems/find-median-from-data-stream/)
@ -70,12 +70,19 @@ Many algorithms relevant in coding interviews make heavy use of recursion - bina
In some cases, you may be computing the result for previously computed inputs. Let's look at the Fibonacci example again. `fib(5)` calls `fib(4)` and `fib(3)`, and `fib(4)` calls `fib(3)` and `fib(2)`. `fib(3)` is being called twice! If the value for `fib(3)` is memoized and used again, that greatly improves the efficiency of the algorithm and the time complexity becomes O(n).
In some cases, you may be computing the result for previously computed inputs. Let's look at the Fibonacci example again. `fib(5)` calls `fib(4)` and `fib(3)`, and `fib(4)` calls `fib(3)` and `fib(2)`. `fib(3)` is being called twice! If the value for `fib(3)` is memoized and used again, that greatly improves the efficiency of the algorithm and the time complexity becomes O(n).
## Recommended questions
## Essential questions
_These are essential questions to practice if you're studying for this topic._
- [Letter Combinations of a Phone Number](https://leetcode.com/problems/letter-combinations-of-a-phone-number/)
@ -83,11 +83,18 @@ When a given sequence is in a sorted order (be it ascending or descending), usin
[Counting sort](https://en.wikipedia.org/wiki/Counting_sort) is a non-comparison-based sort you can use on numbers where you know the range of values beforehand. Examples: [H-Index](https://leetcode.com/problems/h-index/)
[Counting sort](https://en.wikipedia.org/wiki/Counting_sort) is a non-comparison-based sort you can use on numbers where you know the range of values beforehand. Examples: [H-Index](https://leetcode.com/problems/h-index/)
## Recommended questions
## Essential questions
_These are essential questions to practice if you're studying for this topic._
@ -115,11 +115,18 @@ When a question is about counting the number of palindromes, a common trick is t
- For substrings, you can terminate early once there is no match
- For substrings, you can terminate early once there is no match
- For subsequences, use dynamic programming as there are overlapping subproblems. Check out [this question](https://leetcode.com/problems/longest-palindromic-subsequence/)
- For subsequences, use dynamic programming as there are overlapping subproblems. Check out [this question](https://leetcode.com/problems/longest-palindromic-subsequence/)
## Recommended questions
## Essential questions
_These are essential questions to practice if you're studying for this topic._
- [Binary Tree Maximum Path Sum](https://leetcode.com/problems/binary-tree-maximum-path-sum/)
- [Binary Tree Maximum Path Sum](https://leetcode.com/problems/binary-tree-maximum-path-sum/)
- [Binary Tree Level Order Traversal](https://leetcode.com/problems/binary-tree-level-order-traversal/)
- [Binary Tree Level Order Traversal](https://leetcode.com/problems/binary-tree-level-order-traversal/)
- [Lowest Common Ancestor of a Binary Tree](https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-tree/)
- [Lowest Common Ancestor of a Binary Tree](https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-tree/)
@ -152,7 +162,6 @@ If the question involves summation of nodes along the way, be sure to check whet
- [Construct Binary Tree from Preorder and Inorder Traversal](https://leetcode.com/problems/construct-binary-tree-from-preorder-and-inorder-traversal/)
- [Construct Binary Tree from Preorder and Inorder Traversal](https://leetcode.com/problems/construct-binary-tree-from-preorder-and-inorder-traversal/)
- [Serialize and Deserialize Binary Tree](https://leetcode.com/problems/serialize-and-deserialize-binary-tree/)
- [Serialize and Deserialize Binary Tree](https://leetcode.com/problems/serialize-and-deserialize-binary-tree/)
- Binary search tree
- Binary search tree
- [Lowest Common Ancestor of a Binary Search Tree](https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-search-tree/)
@ -52,9 +52,16 @@ Be familiar with implementing from scratch, a `Trie` class and its `add`, `remov
Sometimes preprocessing a dictionary of words (given in a list) into a trie, will improve the efficiency of searching for a word of length k, among n words. Searching becomes O(k) instead of O(n).
Sometimes preprocessing a dictionary of words (given in a list) into a trie, will improve the efficiency of searching for a word of length k, among n words. Searching becomes O(k) instead of O(n).
## Recommended questions
## Essential questions
_These are essential questions to practice if you're studying for this topic._
import InDocAd from './\_components/InDocAd'; import QuestionList from './\_components/QuestionList';
import QuestionList from './\_components/QuestionList';
One of the most important questions to answer at the start of your coding interview preparation is: What study topics and practice questions should you do to most efficiently prepare for your coding interviews?
One of the most important questions to answer at the start of your coding interview preparation is: What study topics and practice questions should you do to most efficiently prepare for your coding interviews?
@ -58,7 +57,7 @@ Keep the estimate relatively conservative so you don't end up burning out.
### Week 1 - 4: Topical study + practice
### Week 1 - 4: Topical study + practice
These are all the topics you should study, in order of priority. The learning resources linked are my algorithm cheatsheets - which give you an overview of must-remembers like time complexity, corner cases and topic-specific useful techniques, as well as must-do practice questions.
These are all the topics you should study, in order of priority. The learning resources linked are my algorithm cheatsheets - which give you an overview of must-remembers like time complexity, corner cases, and topic-specific useful techniques, as well as essential and recommended practice questions.
Don't forget to apply behaviors from [coding interview best practices](./coding-interview-cheatsheet.md) and methods from [coding interview techniques](./coding-interview-techniques.md) early on while you practice!
Don't forget to apply behaviors from [coding interview best practices](./coding-interview-cheatsheet.md) and methods from [coding interview techniques](./coding-interview-techniques.md) early on while you practice!
@ -106,10 +105,13 @@ Don't forget to apply behaviors from [coding interview best practices](./coding-
Here, I listed 75 questions that you should do to be fully prepared for your coding interviews. This list of questions are generated from the [**Grind 75 tool**](https://www.techinterviewhandbook.org/grind75/) (built by me), which generates recommended study plans for coding interviews based on the time you have left. More options like filtering by difficulty, topics, alternative grouping of questions can be found there.
Here, I listed 75 questions that you should do to be fully prepared for your coding interviews. This list of questions are generated from the [**Grind 75 tool**](https://www.techinterviewhandbook.org/grind75/) (built by me), which generates recommended study plans for coding interviews based on the time you have left. More options like filtering by difficulty, topics, alternative grouping of questions can be found there.
Feel free to skip the dynamic programming questions if you haven't studied them or feel that they won't be relevant. Many dynamic programming questions can be solved with recursion / backtracking anyway.
- If you followed the Week 1 - 4 study plan, you would have done some of these questions here. Feel free to skip them or do them again.
- Feel free to skip the dynamic programming questions if you haven't studied them or feel that they won't be relevant. Many dynamic programming questions can be solved with recursion / backtracking anyway.
Don't forget to apply behaviors from [coding interview best practices](./coding-interview-cheatsheet.md) and methods from [coding interview techniques](./coding-interview-techniques.md) early on while you practice!
Don't forget to apply behaviors from [coding interview best practices](./coding-interview-cheatsheet.md) and methods from [coding interview techniques](./coding-interview-techniques.md) early on while you practice!
We recommend using the [**Grind 75**](https://www.techinterviewhandbook.org/grind75/) tool which allows you to keep track of your practice progress.
<QuestionList/>
<QuestionList/>
## Factor time for your self introduction, final questions and mock coding interviews
## Factor time for your self introduction, final questions and mock coding interviews
Yangshun Tay
3 years ago