💻🧠 10 CODING PATTERNS EVERY BEGINNER SHOULD LEARN FOR INTERVIEWS

If you're preparing for coding interviews, don't try to memorize hundreds of solutions.

A better strategy is to learn problem-solving patterns.

Once you recognize the pattern, many seemingly different problems become much easier.

Here are 10 important ones 👇

1️⃣ TWO POINTERS

Use two pointers to move through a data structure, often from opposite ends or at different speeds.

Commonly useful for:

• Sorted arrays

• Pair-sum problems

• Removing duplicates

• Palindrome problems

👉 Instead of repeatedly searching the entire array, two pointers can often reduce unnecessary work.

2️⃣ SLIDING WINDOW

Use a moving window to examine a continuous portion of an array or string.

Useful for problems involving:

• Subarrays

• Substrings

• Maximum/minimum sums

• Longest or shortest ranges

Example idea: ""[1][2][3][4][5]

Instead of recalculating every range from scratch, maintain a window and update it as it moves.

3️⃣ HASHING

Use a Hash Map or Set when you need fast lookup.

Useful for:

• Finding duplicates

• Counting frequencies

• Checking whether an element exists

• Finding pairs

• Tracking previously seen values

👉 If a problem repeatedly asks "Have I seen this before?", think about hashing.

4️⃣ BINARY SEARCH

Binary Search repeatedly divides a sorted search space into two parts.

Instead of checking: 1 → 2 → 3 → 4 → 5 →... you eliminate half of the remaining possibilities after each comparison.

Time Complexity: O(log n)

It can also be applied to certain problems where you're searching for the answer within a monotonic range.

5️⃣ FAST & SLOW POINTERS

Two pointers move at different speeds.

This pattern is commonly used with linked lists to:

• Detect cycles

• Find the middle node

• Determine certain positional relationships

A classic example is the cycle-detection technique using a slow pointer and a fast pointer.

6️⃣ STACK

A stack follows: LIFO → Last In, First Out

Stacks are useful for:

• Valid parentheses

• Undo operations

• Expression evaluation

• Backtracking

• Monotonic stack problems

If you need to process the most recently added item first, consider a stack.

7️⃣ BFS & DFS

These are fundamental ways to traverse trees and graphs.

BFS → Breadth-First Search

Explores nodes level by level.

Often useful for:

• Shortest path in an unweighted graph

• Level-order traversal

• Finding nearby nodes

DFS → Depth-First Search

Explores as deeply as possible before backtracking.

Often useful for:

• Tree traversal

• Graph traversal

• Connected components

• Backtracking-style problems

8️⃣ BACKTRACKING

Backtracking builds a solution step by step.

When a choice doesn't work, you undo it and try another possibility.

Common problems: Permutations, Combinations, Subsets, Sudoku, N-Queens

Basic idea: Choose → Explore → Undo

9️⃣ GREEDY

A greedy algorithm makes the best-looking choice at the current step.

The key question is: "Can making the best local choice lead to a globally optimal solution?"

Greedy approaches appear in problems involving:

• Scheduling

• Intervals

• Resource allocation

• Optimization

⚠️ Not every optimization problem can be solved greedily.