DSA Made Simple: The 80/20 Approach to LeetCode interviewing Success

July 30, 2025

The Core Philosophy

The 80/20 rule in DSA means that roughly 20% of patterns and techniques solve about 80% of coding interview problems. Instead of memorizing hundreds of algorithms, we'll focus on pattern recognition and the most impactful techniques.

Phase 1: The Essential Data Structures

Arrays and Strings - Your Foundation

Why these matter: Arrays appear in 40%+ of all problems, either directly or as components of other structures.

Core Pattern Recognition:

  • Two Pointers: When you see "find pair", "reverse", "palindrome", or "remove duplicates"
  • Sliding Window: When you see "subarray/substring", "maximum/minimum in window", or "k-something"
  • Prefix Sums: When you see "range sum", "subarray sum equals k", or cumulative operations

Key Techniques to Master:

# Two Pointers Template
def two_pointers_template(arr):
    left, right = 0, len(arr) - 1
    while left < right:
        # Process current state
        if condition_met:
            return result
        elif need_larger_sum:
            left += 1
        else:
            right -= 1
    return default_result

# Sliding Window Template (Fixed size window)
def sliding_window_template(arr, k):
    window_sum = sum(arr[:k])  # Initial window
    max_sum = window_sum
    
    for i in range(k, len(arr)):
        window_sum = window_sum - arr[i-k] + arr[i]  # Slide window
        max_sum = max(max_sum, window_sum)
    
    return max_sum

# Prefix Sum Template
def prefix_sum_template(arr):
    # Build prefix sum array: prefix[i] = sum of elements from 0 to i-1
    prefix = [0]  # Start with 0 for easier range sum calculation
    for num in arr:
        prefix.append(prefix[-1] + num)
    
    # Now prefix[j+1] - prefix[i] gives sum of arr[i:j+1]
    return prefix

# Range Sum Query using Prefix Sums
def range_sum_query(prefix_sums, left, right):
    # Sum of elements from index left to right (inclusive)
    return prefix_sums[right + 1] - prefix_sums[left]

# Subarray Sum Equals K - Classic prefix sum + hash map pattern
def subarray_sum_equals_k(arr, k):
    from collections import defaultdict
    
    prefix_sum = 0
    sum_count = defaultdict(int)
    sum_count[0] = 1  # Important: empty subarray has sum 0
    result = 0
    
    for num in arr:
        prefix_sum += num
        # If (prefix_sum - k) exists, we found subarrays ending at current position
        if (prefix_sum - k) in sum_count:
            result += sum_count[prefix_sum - k]
        sum_count[prefix_sum] += 1
    
    return result

Hash Tables - Your Speed Boost

Pattern Recognition: When you need O(1) lookups, counting frequencies, or checking existence.

Common Signatures:

  • "Count occurrences" → Use Counter or defaultdict
  • "Two sum variations" → Store complement in hash map
  • "Anagram problems" → Use character frequency maps

Phase 2: The Power Structures

Linked Lists - Master the Fundamentals

Why focus here: Linked list manipulation teaches pointer logic essential for trees and graphs.

Core Patterns:

  • Fast/Slow Pointers (Floyd's Algorithm): Cycle detection, finding middle
  • Dummy Head Technique: Simplifies edge cases in list manipulation
  • Reversal Patterns: Foundation for many advanced problems
# Fast/Slow Pointer Template - The Swiss Army Knife
def find_middle_or_cycle(head):
    slow = fast = head
    
    while fast and fast.next:
        slow = slow.next
        fast = fast.next.next
        
        # For cycle detection
        if slow == fast:
            return True  # Cycle found
    
    return slow  # Middle node (if no cycle)

Trees - The Gateway to Advanced Problems

Focus on Binary Trees first - they teach the recursive thinking needed for all tree problems.

Essential Patterns:

  • DFS Traversals: Pre/In/Post-order (master recursively first, then iteratively)
  • Level-order Traversal: BFS using queue
  • Path Problems: Root-to-leaf paths, path sums
  • Tree Construction: From traversals, from arrays

The Universal Tree Template:

def tree_dfs_template(root):
    if not root:
        return base_case_value
    
    # Process current node (pre-order)
    current_result = process(root.val)
    
    # Recursive calls
    left_result = tree_dfs_template(root.left)
    right_result = tree_dfs_template(root.right)
    
    # Combine results (post-order processing)
    return combine(current_result, left_result, right_result)

Phase 3: The Algorithm Patterns

Binary Search - Beyond Simple Searching

Key Insight: Binary search isn't just for sorted arrays - it's for any monotonic function.

Pattern Recognition:

  • "Find first/last occurrence"
  • "Search in rotated array"
  • "Find peak element"
  • "Minimum in sorted array"

The Universal Binary Search Template:

def binary_search_template(arr, target):
    left, right = 0, len(arr) - 1
    
    while left <= right:  # Note: <= for exact search
        mid = left + (right - left) // 2  # Avoid overflow
        
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    
    return -1  # Not found

Dynamic Programming - The Pattern Master

80/20 Focus: Master the decision-making framework, not specific problems.

Core Patterns:

  1. Linear DP: Fibonacci, climbing stairs, house robber
  2. 2D DP: Unique paths, edit distance, longest common subsequence
  3. Knapsack Variants: 0/1 knapsack, coin change, partition problems

The DP Thinking Framework:

  1. Identify the decision at each step
  2. Define state (what information do you need to make the decision?)
  3. Write recurrence relation
  4. Handle base cases
  5. Optimize space if needed
# Bottom-up DP Template
def dp_template(problem_input):
    # Step 1: Initialize DP table
    dp = [0] * (problem_size + 1)
    
    # Step 2: Base cases
    dp[0] = base_case_value
    
    # Step 3: Fill table using recurrence relation
    for i in range(1, problem_size + 1):
        for each_possible_choice:
            dp[i] = optimize(dp[i], dp[previous_state] + cost)
    
    return dp[problem_size]

Phase 4: Graph Algorithms

BFS and DFS - The Graph Explorers

Pattern Recognition:

  • BFS: Shortest path in unweighted graphs, level-by-level exploration
  • DFS: Path finding, cycle detection, topological sorting

When to use which:

  • BFS: "Shortest path", "minimum steps", "level order"
  • DFS: "All paths", "can reach", "cycle detection"
# BFS Template
from collections import deque

def bfs_template(graph, start):
    visited = set()
    queue = deque([start])
    visited.add(start)
    
    while queue:
        node = queue.popleft()
        # Process node
        
        for neighbor in graph[node]:
            if neighbor not in visited:
                visited.add(neighbor)
                queue.append(neighbor)

# DFS Template
def dfs_template(graph, node, visited):
    if node in visited:
        return
    
    visited.add(node)
    # Process node
    
    for neighbor in graph[node]:
        dfs_template(graph, neighbor, visited)

The Pattern Recognition Framework

How to Approach Any Problem

  1. Read and understand - What is the problem really asking?
  2. Identify the data structure - What are you working with?
  3. Look for keywords - "subarray", "path", "cycle", "shortest", "maximum"
  4. Match to patterns - Which template fits?
  5. Code the solution - Start with the template, then customize

Common Problem Signatures

  • "Two Sum" variations → Hash map for O(1) lookups
  • "Palindrome" problems → Two pointers or expand around center
  • "Subarray/substring" with constraints → Sliding window
  • "Path in tree/graph" → DFS with backtracking
  • "Shortest path/minimum steps" → BFS
  • "Count ways to do X" → Dynamic programming
  • "Find in sorted/rotated array" → Binary search

Practice Strategy

Week-by-Week Focus

Weeks 1-2: Arrays, strings, hash tables (solve 3-4 problems daily) Weeks 3-4: Linked lists, trees (solve 2-3 problems daily, focus on understanding) Weeks 5-6: Binary search, DP (solve 2-3 problems daily, emphasize pattern recognition) Weeks 7-8: Graphs, advanced problems (solve 2-3 problems daily, integrate all patterns)

Problem Selection Strategy

Start with these high-frequency patterns:

  1. Two Sum and variations
  2. Valid Parentheses
  3. Merge Two Sorted Lists
  4. Maximum Subarray
  5. Climbing Stairs
  6. Binary Tree Inorder Traversal
  7. Best Time to Buy/Sell Stock
  8. Number of Islands

Remember: The goal isn't to memorize solutions, but to recognize patterns and apply templates. Each pattern you master unlocks dozens of similar problems.

Mental Models for Success

Think of DSA patterns like learning to drive:

  • Data structures are your vehicle controls (steering wheel, pedals)
  • Algorithms are your driving techniques (parallel parking, highway merging)
  • Pattern recognition is reading traffic situations and knowing which technique to apply

Master the fundamentals deeply rather than trying to learn everything superficially. A strong foundation in these core patterns will serve you better than memorizing 500 different solutions.

Advanced Mastery: Beyond the Basics

Level 2 Patterns (After mastering the fundamentals)

Once you've internalized the core patterns, these advanced techniques will unlock the remaining 20% of problems that require deeper algorithmic thinking.

Graph Advanced Patterns

Union-Find (Disjoint Set): Master this for connectivity problems, detecting cycles in undirected graphs, and dynamic connectivity queries. The pattern appears in problems about connected components, network connectivity, and redundant connections.

Topological Sorting: Essential for dependency resolution problems, course scheduling, and any scenario involving precedence relationships. Recognize this pattern when you see "prerequisites," "ordering," or "dependency" in problem descriptions.

Minimum Spanning Tree (MST): Kruskal's and Prim's algorithms become crucial for optimization problems involving connecting all nodes with minimum cost. Watch for problems about connecting cities, network design, or finding minimum cost to connect all points.

String Algorithms

KMP (Knuth-Morris-Pratt) Algorithm: While regex and built-in functions handle most string matching, understanding KMP helps with pattern matching problems and teaches the concept of failure functions that appear in other algorithms.

Trie (Prefix Tree): Critical for prefix-based problems, autocomplete systems, and word searching. Recognize this pattern when dealing with dictionaries, prefix matching, or problems involving multiple string searches.

Advanced Tree Techniques

Segment Trees and Binary Indexed Trees: These handle range query and update operations efficiently. Look for problems involving range sums, range minimum/maximum queries, or frequent updates to array elements.

Tree Traversal with State: Many advanced tree problems require carrying additional state during traversal, such as path information, ancestor data, or subtree properties.

Mathematical Patterns

Number Theory: Prime factorization, GCD/LCM, and modular arithmetic become important for mathematical problems. These often appear in array problems with specific mathematical constraints.

Bit Manipulation: Beyond basic operations, advanced bit techniques like bit masks for subset enumeration and XOR properties for finding unique elements become powerful tools.

Memory Retention Strategies

The Spacing Effect: Your Most Powerful Tool

Human memory works best with spaced repetition rather than cramming. This means revisiting concepts at increasing intervals rather than studying everything once. For DSA mastery, implement a review schedule where you revisit patterns after 1 day, then 3 days, then 1 week, then 2 weeks, then 1 month.

The key insight here is that forgetting is actually part of the learning process. When you struggle to recall a pattern and then successfully remember it, you strengthen the neural pathway much more than if you had simply reviewed it while it was still fresh in memory.

Active Recall Over Passive Review

Instead of re-reading your notes or looking at solutions, practice active recall by covering the solution and trying to reproduce the approach from memory. This might feel more difficult initially, but it creates much stronger retention.

For DSA patterns, this means practicing the "blank page" approach. Given a problem type like "find all paths in a binary tree," can you write the DFS template and explain the approach without looking at any references? This retrieval practice is far more effective than simply reading through code examples.

The Interleaving Technique

Rather than practicing one type of problem for hours, mix different patterns within the same study session. This approach, called interleaving, helps your brain learn to discriminate between different problem types and strengthens pattern recognition.

For example, instead of solving ten sliding window problems in a row, alternate between sliding window, two pointers, and binary search problems. This forces your brain to actively choose the appropriate technique rather than simply applying the same pattern repeatedly.

Creating Meaningful Connections

Connect new patterns to knowledge you already have. For instance, when learning segment trees, relate them to the binary search concept of divide and conquer, or connect dynamic programming to the mathematical concept of recursion with memoization.

These connections create multiple retrieval pathways in your memory. If you forget one way to think about a concept, you can access it through another connected idea.

The Teaching Method

Explain patterns and solutions out loud, either to someone else or to yourself. This verbal processing engages different parts of your brain and reveals gaps in your understanding that silent review might miss.

When you can clearly explain why you chose a particular approach and walk through each step of the algorithm, you've moved beyond simple memorization to true understanding.

Implementation Variations

Don't just learn one way to implement each pattern. For example, implement binary search both recursively and iteratively, or solve tree problems using both recursive DFS and iterative approaches with explicit stacks.

These variations strengthen your understanding of the underlying concepts and make you more adaptable during interviews when you might need to adjust your approach based on follow-up questions or constraints.

Mistake Analysis and Recovery

Keep a log of problems where you initially chose the wrong approach or made implementation errors. Review these periodically to understand your common pitfalls and develop strategies to avoid them.

This metacognitive awareness helps you catch mistakes during interviews and builds confidence in your problem-solving process.

Building Long-Term Expertise

The Spiral Learning Approach

Return to fundamental patterns periodically, but approach them with increased depth each time. Your initial understanding of two pointers might focus on the basic template, but later iterations should include edge case handling, optimization techniques, and variants like three pointers or multiple sliding windows.

This spiral approach ensures that your foundational knowledge continues to deepen rather than becoming stale, and it helps you see connections between basic and advanced patterns.

Real-World Application Context

Whenever possible, understand how DSA patterns apply to real systems and problems. For example, understanding that tries are used in autocomplete systems, or that graph algorithms power social network analysis, creates richer memory traces and makes the learning more meaningful.

This contextual knowledge also helps during interviews when discussing trade-offs and real-world applications of your solutions.