Coding Interview Prep — LeetCode Strategy & Patterns (2026)

In this guide, you'll learn a structured approach to Coding Interview Prep — the 14 essential patterns, a study plan, and strategies that turn LeetCode grinding into genuine problem-solving skill. Top tech companies (FAANG, startups) use coding interviews to assess problem-solving ability, and candidates who prepare systematically see offers ranging from $100,000–$400,000+. The algorithms and patterns covered here are used daily at DodaTech for tasks like route optimization in Doda Browser and pattern matching in Durga Antivirus Pro.

The Role

Coding interviews test your ability to solve algorithmic problems under time pressure. You're evaluated on correctness, efficiency, code quality, and communication. The goal isn't to memorize solutions — it's to demonstrate structured thinking and familiarity with common patterns.

The 14 Essential Patterns

Based on "Grokking Coding Interview Patterns" and validated by thousands of interview experiences:

1. Two Pointers

Used for sorted arrays and linked lists. One pointer starts at the beginning, another at the end (or both at the beginning with different speeds). Common problems: two-sum in sorted array, container with most water, remove duplicates.

2. Sliding Window

Efficient for subarray/substring problems. Expand the window while the condition holds, shrink when it doesn't. Common: longest substring without repeating characters, maximum sum subarray of size K, minimum window substring.

3. Fast & Slow Pointers (Tortoise & Hare)

Detect cycles in linked lists, find middle of a linked list. One pointer moves twice as fast as the other.

4. Merge Intervals

Problems involving overlapping intervals. Sort by start time, then merge overlapping intervals. Common: merge intervals, insert interval, meeting rooms.

5. Cyclic Sort

For problems where numbers are in a fixed range (1–n). Place each number at its correct index. Common: find missing number, find all duplicates.

6. In-place Reversal of a Linked List

Reverse a linked list, reverse a sub-list. Keep track of previous, current, and next pointers.

7. Tree BFS

Level-order traversal using a queue. Common: binary tree level order traversal, zigzag traversal, minimum depth.

8. Tree DFS

Preorder, inorder, postorder using recursion or stack. Common: path sum, validate BST, lowest common ancestor.

9. Two Heaps

Use a max-heap and min-heap simultaneously. Common: find median of a data stream, Sliding Window median.

10. Subsets

Generate all subsets/permutations/combinations. Usually solved with recursion + Backtracking, or BFS for subsets.

11. Modified Binary Search

Variations of binary search on sorted arrays. Common: search in rotated array, find first/last occurrence, find peak element.

12. Top K Elements

Find top K frequent elements, K closest points. Use heap or quickselect. Common: top K frequent words, K closest points to origin.

13. K-way Merge

Merge K sorted lists using a min-heap. Common: merge K sorted lists, smallest range covering K lists.

14. 0/1 Knapsack (Dynamic Programming)

Classic DP pattern. For each item, decide to include it or skip it. Common: subset sum, partition equal subset sum, coin change.

Study Plan (12 Weeks)

Weeks 1–4: Foundations

• Learn Big O notation and time complexity analysis
• Practice arrays, strings, hash tables, recursion
• Master Two Pointers, Sliding Window, Fast & Slow Pointers (20 problems)

Weeks 5–8: Core Data Structures

• Trees and graphs: BFS, DFS, tree traversals
• Linked lists: reversal, cycle detection
• Heaps, queues, stacks
• Modified Binary Search (25 problems)

Weeks 9–12: Advanced Patterns

• Dynamic Programming: 0/1 Knapsack, Longest Common Subsequence, Palindromes
• Backtracking: permutations, combinations, N-Queens
• Top K Elements, K-way Merge, Subsets (25 problems)

Spend 1–2 hours daily. Do 70–100 problems total for solid preparation.

Learning Path

Free Resources

• NeetCode — Pattern-based LeetCode solutions with explanations
• LeetCode Explore Cards — Structured learning paths
• Visualgo.net — Algorithm visualizations

Paid Courses

• Grokking Coding Interview Patterns (Design Gurus) — The 14 patterns
• AlgoExpert — Curated problems with video solutions
• ByteByteGo — System Design + coding patterns

Books

• Cracking the Coding Interview by Gayle Laakmann McDowell
• Elements of Programming Interviews by Aziz, Lee, Prakash
• Introduction to Algorithms (CLRS) — Reference, not Interview Prep

Portfolio / Track Record

Build a LeetCode streak (100+ days), share your solutions on GitHub, and contribute explanations to discussion threads. Top performers often maintain a "LeetCode patterns" repo with categorized solutions.

Getting the Job

Resume

List LeetCode rating or coding competition achievements if notable. But focus resume on real engineering impact — coding interviews are passed by practice, not resume lines.

Interview Strategy

• Clarify: Repeat the problem, ask about edge cases, constraints
• Brute force: Start with the simplest solution, then optimize
• Test: Walk through your solution with example inputs
• Optimize: Analyze time/space complexity, suggest improvements
• Code cleanly: Meaningful variable names, helper functions, handle edge cases

Mock Interviews

Practice with friends, use Pramp or interviewing.io for free mock interviews, and use LeetCode's mock interview feature. Do at least 5 mock interviews before real ones.

Career Progression

flowchart LR
  A[LeetCode Novice] --> B[50 problems done]
  B --> C[100+ problems, knows patterns]
  C --> D[200+ problems, mock interviews]
  D --> E[Interview-ready]
  E --> F[Offer / No Offer]
  F -->|No Offer| D
  F -->|Offer| G[Senior Engineer]

Practice Questions

1. What is the time complexity of binary search?

O(log n) — each step halves the search space. For a sorted array of size n, you need at most log₂(n) comparisons.

2. Explain the difference between BFS and DFS.

BFS explores level by level (queue), finding the shortest path in unweighted graphs. DFS explores depth-first (stack/recursion), useful for exhausting possibilities in Backtracking. BFS memory scales with branching factor; DFS memory scales with depth.

3. What is Dynamic Programming and when do you use it?

DP solves problems by breaking them into overlapping subproblems and reusing results. Use it when a problem has optimal substructure (optimal solution depends on optimal sub-solutions) and overlapping subproblems. Common indicators: "find the maximum/minimum," "number of ways," "true/false if possible."

4. How do you detect a cycle in a linked list?

Use fast and slow pointers (Floyd's algorithm). Start both at head. Slow moves one step, fast moves two steps. If they meet, there's a cycle. If fast reaches null, no cycle. O(n) time, O(1) space.

5. What is the difference between a stack and a queue?

Stack is LIFO (Last In, First Out) — like a stack of plates. Queue is FIFO (First In, First Out) — like a line at a store. Stacks are used for DFS, function calls, undo operations. Queues are used for BFS, task scheduling, breadth-first traversal.

Challenge

Implement a text justification algorithm: given an array of words and a max width per line, format the text so each line has exactly the specified width, with extra spaces distributed evenly between words or at the end for the last line.

Real-World Task

Take a real-world feature you've built and express its core logic as an algorithmic problem. Identify the pattern it follows (two pointers, Sliding Window, etc.) and implement it in a bug-free, optimized way with test cases.

FAQ