The 4-Week LeetCode Study Plan That Gets You Hired (2026)

The problem with random grinding Ask any engineer who has failed a FAANG loop despite solving 250 LeetCode problems: it's entirely possible to grind hundreds of problems and still be unable to reliably solve a medium you haven't seen before. The issue isn't effort — it's approach. Random grinding builds a library of solutions you've memorized. Structured prep builds pattern recognition — the ability to look at a new problem you've never seen and immediately recognize which of a dozen patterns it belongs to, then apply the appropriate template. This 4-week plan is built around that principle. The goal isn't to have seen the problem before. The goal is to recognize the shape of the problem. ## Rules before you start **Rule 1:** Solve every problem twice — once now, once 48 hours later without looking at your previous solution. Retention requires repetition. **Rule 2:** Time-box your struggle. Spend 15–20 minutes genuinely trying before you look at a solution. Less than that and you don't build problem-solving instincts. More than that and you're wasting time. **Rule 3:** Practice out loud. Coding silently at your keyboard is a fundamentally different skill from coding while explaining your approach. Start narrating your thinking from day one. **Rule 4:** Track patterns, not problems. After each problem, write one sentence: "This was a [pattern] problem. The signal was [X]." ## Week 1: Arrays, strings, and hash maps **Days 1–2: Two pointers** The two-pointer pattern reduces O(n²) brute-force solutions to O(n) by using two indices moving through an array in coordinated ways. Two categories: same-direction (sliding window variation) and opposite-direction (converging pointers). Problems: Two Sum II, 3Sum, Container With Most Water, Trapping Rain Water, Move Zeroes, Valid Palindrome. Signal: "Find pair/triplet," "sorted array," "from both ends." **Days 3–4: Sliding window** For subarray and substring problems with a constraint. The window expands to include elements and shrinks when the constraint is violated. Problems: Longest Substring Without Repeating Characters, Minimum Window Substring, Longest Subarray With K Ones, Fruit Into Baskets, Permutation in String. Signal: "Longest/shortest subarray/substring with [condition]." **Day 5: Hash maps** O(1) lookup transforms many O(n²) problems. When you see "find duplicates," "count frequencies," or "check membership quickly" — hash map. Problems: Group Anagrams, Top K Frequent Elements, Longest Consecutive Sequence, Subarray Sum Equals K. **Daily target:** 4–5 problems. Understand the pattern, not just the solution. ## Week 2: Trees and graphs **Days 1–2: Binary trees** Master both recursive and iterative traversals before anything else. The recursive pattern (solve left subtree, solve right subtree, combine results) applies to a remarkable number of tree problems. Problems: Maximum Depth, Invert Binary Tree, Diameter of Binary Tree, Level Order Traversal (BFS), Lowest Common Ancestor, Validate BST, Symmetric Tree, Path Sum. **Day 3: Binary search trees** Problems: Kth Smallest Element in BST, Convert Sorted Array to BST, Delete Node in BST. **Days 4–5: Graphs** BFS for shortest path and level-by-level traversal. DFS for connectivity, cycle detection, and exploring all paths. Problems: Number of Islands, Clone Graph, Course Schedule I and II (topological sort), Word Ladder (BFS), Pacific Atlantic Water Flow, Number of Connected Components, Accounts Merge (Union-Find). ## Week 3: Dynamic programming and binary search **Days 1–2: 1D dynamic programming** DP problems have two properties: optimal substructure (optimal solution built from optimal sub-solutions) and overlapping subproblems (same sub-problems computed repeatedly). When you see these, reach for DP. Problems: Climbing Stairs, House Robber I and II, Coin Change, Jump Game, Longest Increasing Subsequence, Word Break. **Days 3–4: 2D dynamic programming** Problems: Unique Paths, Minimum Path Sum, Longest Common Subsequence, Edit Distance, 0/1 Knapsack. **Day 5: Binary search** Beyond searching sorted arrays — binary search on the answer. When you can frame "find the minimum X such that [condition]" or "find the maximum X such that [condition]," you can binary search even when there's no explicit sorted array. Problems: Find Minimum in Rotated Sorted Array, Search in Rotated Sorted Array, Koko Eating Bananas, Capacity to Ship Packages, Find Peak Element. ## Week 4: Stacks, heaps, and mock interviews **Days 1–2: Stacks and monotonic stacks** The monotonic stack maintains a stack where elements are always in increasing (or decreasing) order. It solves "next greater/smaller element" problems in O(n). Problems: Valid Parentheses, Daily Temperatures, Largest Rectangle in Histogram, Trapping Rain Water (stack approach), Next Greater Element. **Day 3: Heaps and priority queues** Use a min-heap for "K smallest" problems, a max-heap for "K largest." Use a heap whenever you need repeated min/max extraction from a dynamic set. Problems: Top K Frequent Elements (heap approach), Merge K Sorted Lists, K Closest Points to Origin, Task Scheduler, Find Median from Data Stream. **Days 4–7: Full mock interviews** Set a 45-minute timer. Pick two problems you haven't seen. Solve them while talking out loud, explaining your approach, complexity, and any alternatives. Then pick 3 behavioral questions and answer them using STAR. Do at least 4 full mocks this week. Preciprocal's AI mock interview simulates a real technical interview with an interviewer who asks follow-up questions and scores your communication alongside your solution quality. ## The uncomfortable truth about prep time Four weeks of structured prep beats twelve weeks of random grinding. But four weeks of structured prep requires genuine focus — 2–3 hours per day minimum. If you can't commit to that, extend the timeline and maintain the structure. The structure is the point.