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10 Must-Solve LeetCode Problems for Freshers Preparing for Tech Interviews

Preparing for tech interviews can be daunting, but mastering a set of well-chosen LeetCode problems can significantly boost your confidence and problem-solving skills. Below is a curated list of 10 must-solve LeetCode problems that cover essential concepts like arrays, linked lists, dynamic programming, and binary search. These problems are frequently asked in interviews and will help you build a strong foundation in coding efficiency and algorithmic thinking.

1. Two Sum

Problem Statement: Given an array of integers, find two numbers that add up to a specific target.
Key Concept: Hash maps for efficient O(n) solutions.
Solution:

def twoSum(nums, target):
    num_dict = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in num_dict:
            return [num_dict[complement], i]
        num_dict[num] = i

Why It’s Important: This problem introduces hash maps, a fundamental data structure for optimizing lookups. It’s a staple in interview problem sets like Blind 75.

2. Reverse Linked List

Problem Statement: Reverse a singly linked list.
Key Concept: Pointer manipulation in linked lists.
Solution:

def reverseList(head):
    prev = None
    current = head
    while current:
        next_temp = current.next
        current.next = prev
        prev = current
        current = next_temp
    return prev

Why It’s Important: This problem tests your ability to handle pointers and memory efficiently, a critical skill for working with linked lists.

3. Maximum Subarray

Problem Statement: Find the contiguous subarray with the largest sum.
Key Concept: Kadane’s algorithm for O(n) optimization.
Solution:

def maxSubArray(nums):
    max_current = max_global = nums[0]
    for i in range(1, len(nums)):
        max_current = max(nums[i], max_current + nums[i])
        max_global = max(max_global, max_current)
    return max_global

Why It’s Important: This problem is a classic example of dynamic programming and is frequently used to test optimization skills.

4. Merge Two Sorted Lists

Problem Statement: Merge two sorted linked lists into one sorted list.
Key Concept: Combining linked lists with O(n+m) time complexity.
Solution:

def mergeTwoLists(l1, l2):
    dummy = ListNode(0)
    current = dummy
while l1 and l2:
        if l1.val <= l2.val:
            current.next = l1
            l1 = l1.next
        else:
            current.next = l2
            l2 = l2.next
        current = current.next
    current.next = l1 if l1 else l2
    return dummy.next

Why It’s Important: This problem tests your ability to manipulate pointers and merge data structures efficiently.

5. Binary Search

Problem Statement: Implement binary search on a sorted array.
Key Concept: Divide-and-conquer strategy for O(log n) time complexity.
Solution:

def binarySearch(nums, target):
    left, right = 0, len(nums) - 1
    while left <= right:
        mid = (left + right) // 2
        if nums[mid] == target:
            return mid
        elif nums[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1

Why It’s Important: Binary search is a fundamental algorithm for efficiently searching sorted data.

6. First Bad Version

Problem Statement: Given a series of versions, find the first bad version using the least number of API calls.
Key Concept: Binary search for efficient version tracking.
Solution:

def firstBadVersion(n):
    left, right = 1, n
    while left < right:
        mid = (left + right) // 2
        if isBadVersion(mid):
            right = mid
        else:
            left = mid + 1
    return left

Why It’s Important: This problem demonstrates the practical application of binary search in real-world scenarios.

7. Climbing Stairs

Problem Statement: Find the number of ways to climb a staircase with n steps, taking 1 or 2 steps at a time.
Key Concept: Dynamic programming for step-by-step optimization.
Solution:

def climbStairs(n):
    if n <= 2:
        return n
    dp = [0] * (n + 1)
    dp[1] = 1
    dp[2] = 2
    for i in range(3, n + 1):
        dp[i] = dp[i-1] + dp[i-2]
    return dp[n]

Why It’s Important: This problem is a classic introduction to dynamic programming and recursive thinking.

8. Best Time to Buy and Sell Stock

Problem Statement: Maximize profit by buying and selling a stock at the right time.
Key Concept: Single-pass optimization for O(n) time complexity.
Solution:

def maxProfit(prices):
    min_price = float('inf')
    max_profit = 0
    for price in prices:
        min_price = min(min_price, price)
        max_profit = max(max_profit, price - min_price)
    return max_profit

Why It’s Important: This problem tests your ability to optimize solutions and handle real-world financial scenarios.

9. Valid Parentheses

Problem Statement: Check if a string of parentheses is valid.
Key Concept: Stack-based string validation.
Solution:

def isValid(s):
    stack = []
    pairs = {')': '(', '}': '{', ']': '['}
    for char in s:
        if char in pairs.values():
            stack.append(char)
        elif char in pairs:
            if not stack or stack.pop() != pairs[char]:
                return False
    return len(stack) == 0

Why It’s Important: This problem is a great way to practice stack operations and string manipulation.

10. Rotate Array

Problem Statement: Rotate an array to the right by k steps.
Key Concept: Array manipulation with minimal complexity.
Solution:

def rotate(nums, k):
    n = len(nums)
    k = k % n
    nums[:] = nums[-k:] + nums[:-k]

Why It’s Important: This problem tests your ability to manipulate arrays efficiently and handle edge cases.

How to Approach LeetCode Problems (Step-by-Step)

1. Understand the Problem: Read the problem statement carefully and identify input/output constraints.
2. Break It Down: Divide the problem into smaller, manageable parts.
3. Choose the Right Data Structure: Select the most efficient data structure (e.g., hash maps, stacks, queues).
4. Write Pseudocode: Plan your solution before coding.
5. Implement the Solution: Translate your pseudocode into code.
6. Test Edge Cases: Ensure your solution handles edge cases like empty inputs or large datasets.
7. Optimize: Look for ways to improve time and space complexity.

Final Tips for Success

• Practice Regularly: Consistency is key. Aim to solve at least 1-2 problems daily.
• Use Curated Lists: Focus on problem sets like Blind 75 or Top Interview 150.
• Simulate Interviews: Time yourself and practice explaining your thought process.
• Learn from Mistakes: Review your solutions and understand where you can improve.

By mastering these 10 problems and following a structured approach, you’ll be well-prepared to tackle technical interviews with confidence. Happy coding! 🚀