Leetcode patterns you should definitely checkout to Learn DSA(Java) from scratch
1๏ธโฃ Arrays: Data structures, such as arrays, store elements in contiguous memory locations. They are versatile and useful for a wide variety of purposes.
LeetCode Problems:
โข Search in Rotated Sorted Array (Problem #33)
โข Product of Array Except Self (Problem #238)
โข Find the Missing Number (Problem #268)
2๏ธโฃTwo Pointers: In Two Pointers, two pointers are maintained in the collection and can be manipulated to solve a problem efficiently.
LeetCode problems:
โข Trapping Rain Water (Problem #42)
โข Longest Substring Without Repeating Characters (Problem #3)
โข Squares of a Sorted Array (Problem #977)
3๏ธโฃIn-place Linked List Traversal: As an explanation, in-place traversal is a technique for modifying linked list nodes without using extra space.
LeetCode Problems:
โข Remove Nth Node From End of List (Problem #19)
โข Reorder List (Problem #143)
4๏ธโฃFast & Slow Pointers: This pattern uses two pointers to traverse a sequence at different speeds (fast and slow), often used to detect cycles or find a specific position in the sequence.
LeetCode Problems:
โข Happy Number (Problem #202)
โข Subarray Sum Equals K (Problem #560)
โข Intersection of Two Linked Lists (Problem #160)
5๏ธโฃMerge Intervals: This pattern involves merging overlapping intervals in a collection, often used in problems dealing with intervals or ranges.
LeetCode problems:
โข Non-overlapping Intervals (Problem #435)
โข Minimum Number of Arrows to Burst Balloons (Problem #452)
Join for more: https://t.me/crackingthecodinginterview
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING ๐๐
1๏ธโฃ Arrays: Data structures, such as arrays, store elements in contiguous memory locations. They are versatile and useful for a wide variety of purposes.
LeetCode Problems:
โข Search in Rotated Sorted Array (Problem #33)
โข Product of Array Except Self (Problem #238)
โข Find the Missing Number (Problem #268)
2๏ธโฃTwo Pointers: In Two Pointers, two pointers are maintained in the collection and can be manipulated to solve a problem efficiently.
LeetCode problems:
โข Trapping Rain Water (Problem #42)
โข Longest Substring Without Repeating Characters (Problem #3)
โข Squares of a Sorted Array (Problem #977)
3๏ธโฃIn-place Linked List Traversal: As an explanation, in-place traversal is a technique for modifying linked list nodes without using extra space.
LeetCode Problems:
โข Remove Nth Node From End of List (Problem #19)
โข Reorder List (Problem #143)
4๏ธโฃFast & Slow Pointers: This pattern uses two pointers to traverse a sequence at different speeds (fast and slow), often used to detect cycles or find a specific position in the sequence.
LeetCode Problems:
โข Happy Number (Problem #202)
โข Subarray Sum Equals K (Problem #560)
โข Intersection of Two Linked Lists (Problem #160)
5๏ธโฃMerge Intervals: This pattern involves merging overlapping intervals in a collection, often used in problems dealing with intervals or ranges.
LeetCode problems:
โข Non-overlapping Intervals (Problem #435)
โข Minimum Number of Arrows to Burst Balloons (Problem #452)
Join for more: https://t.me/crackingthecodinginterview
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING ๐๐
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๐กUse ZIP function to iterate over multiple lists simultaneously ๐ก
#pythontips #codingtips #python #pythonprogramming #codesmarter #coding
#pythontips #codingtips #python #pythonprogramming #codesmarter #coding
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Tech interviews ask candidates to invert binary trees while their real job is 90% figuring out why a 3rd-party API returns null sometimes.
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๐ป ๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ ๐๐ถ๐ด ๐ข ๐ป๐ผ๐๐ฎ๐๐ถ๐ผ๐ป!
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
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