Coding Interview Preparation

Master key concepts, strategies, and practical steps to excel in technical interviews.

Popular Coding Platforms

  • LeetCode: Solve problems sorted by difficulty and company-specific questions.
  • HackerRank: Practice coding challenges in various languages and domains.
  • Codeforces: Participate in timed contests to improve problem-solving speed.
  • GeeksforGeeks: Read in-depth articles on data structures, algorithms, and coding patterns.

Key Interview Concepts

  • Data Structures: Arrays, Linked Lists, Stacks, Queues, Hash Tables, Trees, Graphs.
  • Algorithms: Sorting, Searching, Dynamic Programming, Backtracking, Greedy algorithms.
  • Big-O Analysis: Understand time and space complexity to optimize solutions.
  • System Design: For senior roles, practice designing scalable systems.

Example Problem Workflow

Here’s a step-by-step guide for solving a coding problem during an interview:

  • Understand the Problem: Clarify all requirements and constraints.
  • Plan the Solution: Explain your approach to the interviewer, and write pseudocode.
  • Implement the Code: Write clean, modular code while explaining each step.
  • Test Your Code: Use example and edge cases to validate your solution.
  • Optimize: Discuss possible improvements in time or space complexity.

Strategies for Success

  • Practice Daily: Solve at least one coding problem every day to build muscle memory.
  • Focus on Weak Areas: Identify and improve areas where you struggle the most.
  • Mock Interviews: Simulate real interview scenarios using platforms like Pramp or with friends.
  • Learn Common Patterns: Understand common problem-solving patterns like sliding windows, divide-and-conquer, etc.

Sample Problems

Sample Problem #1: Two Sum

Problem: Given an array of integers, return indices of the two numbers such that they add up to a target.

Example:

        Input: nums = [2, 7, 11, 15], target = 9
        Output: [0, 1]
        Explanation: nums[0] + nums[1] = 2 + 7 = 9

Approach: We solve this problem using a hash map to track seen numbers and their indices for efficient lookups.

Step-by-Step Guide to Solving

    • We need to find two numbers in the array that sum up to the target value.
    • Return their indices, not the numbers themselves.
    • The problem guarantees there is exactly one solution.
    • Example Input: nums = [2, 7, 11, 15], target = 9.
  • Plan the Solution:
    • Iterate through the array while maintaining a hash map of seen numbers.
    • For each number:
      • Calculate the complement as `target - num`.
      • Check if the complement exists in the hash map.
      • If found, return the indices of the complement and the current number.
      • If not, store the current number in the hash map.
        def two_sum(nums, target):
            num_map = {}  # Hash map to store numbers and their indices
            for i, num in enumerate(nums):
                complement = target - num  # Calculate complement
                if complement in num_map:  # Check if complement exists
                    return [num_map[complement], i]  # Return indices
                num_map[num] = i  # Store the current number
  • Test the Code:
    • Input: nums = [2, 7, 11, 15], target = 9 -> Output: [0, 1]
    • Input: nums = [3, 2, 4], target = 6 -> Output: [1, 2]
    • Input: nums = [3, 3], target = 6 -> Output: [0, 1]
    • Edge Cases:
      • Array with negative numbers.
      • Array with duplicate numbers.
  • Optimize:
    • Time Complexity: O(n) because each lookup and insertion in the hash map is O(1).
    • Space Complexity: O(n) for storing the hash map.

    • Sample Problem #2: Valid Parentheses

    Problem: Merge two sorted linked lists and return it as a single sorted list.

    Example:

            Input: l1 = [1,2,4], l2 = [1,3,4]
        Output: [1,1,2,3,4,4]

    Approach: Use a dummy node to simplify the merging process, attaching nodes from the input lists in sorted order.

    Step-by-Step Guide to Solving

    • Understand the Problem:
      • Combine two already sorted lists into a single sorted list.
      • Example Input: l1 = [1, 2, 4], l2 = [1, 3, 4].
      • Output: [1, 1, 2, 3, 4, 4].
    • Plan the Solution:
      • Use a dummy node to serve as the starting point.
      • Use a pointer to track the last node in the merged list.
      • Compare the heads of both lists and attach the smaller node to the merged list.
      • Move the pointer in the list from which the smaller node was taken.
      • When one list is exhausted, attach the remainder of the other list.
            class ListNode:
            def __init__(self, val=0, next=None):
                self.val = val
                self.next = next

        def merge_two_lists(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 or l2  # Attach remaining nodes
            return dummy.next
    • Test the Code:
      • Input: s = "()[]{}" -> Output: true
      • Input: s = "(]" -> Output: false
      • Input: s = "([)]" -> Output: false
      • Edge Cases:
        • Empty string -> Valid
        • Only opening brackets -> Invalid
        • Only closing brackets -> Invalid
  • Optimize:
    • Time Complexity: O(n + m), where n and m are the lengths of the input lists.
    • Space Complexity: O(1) as it’s an in-place operation.

    • Sample Problem #3: Merge Two Sorted Lists

    Problem: Merge two sorted linked lists and return it as a single sorted list.

    Example:

            Input: l1 = [1,2,4], l2 = [1,3,4]
        Output: [1,1,2,3,4,4]

    Approach: Use a dummy node to simplify the merging process, attaching nodes from the input lists in sorted order.

    Step-by-Step Guide to Solving

    • Understand the Problem:
      • Combine two already sorted lists into a single sorted list.
      • Example Input: l1 = [1, 2, 4], l2 = [1, 3, 4].
      • Output: [1, 1, 2, 3, 4, 4].
    • Plan the Solution:
      • Use a dummy node to serve as the starting point.
      • Use a pointer to track the last node in the merged list.
      • Compare the heads of both lists and attach the smaller node to the merged list.
      • Move the pointer in the list from which the smaller node was taken.
      • When one list is exhausted, attach the remainder of the other list.
            class ListNode:
            def __init__(self, val=0, next=None):
                self.val = val
                self.next = next

        def merge_two_lists(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 or l2  # Attach remaining nodes
            return dummy.next
    • Test the Code:
      • Input: l1 = [1, 2, 4], l2 = [1, 3, 4] -> Output: [1, 1, 2, 3, 4, 4]
      • Input: l1 = [], l2 = [0] -> Output: [0]
      • Input: l1 = [], l2 = [] -> Output: []
      • Edge Cases:
        • One list is empty, the other is non-empty.
        • Both lists are empty.
  • Optimize:
    • Time Complexity: O(n + m), where n and m are the lengths of the input lists.
    • Space Complexity: O(1) as it’s an in-place operation.

    • Additional Resources