The 8-Week Coding Interview Study Plan

Most people preparing for coding interviews make the same mistake: they open LeetCode, pick a random problem, get stuck, read the solution, and repeat. After weeks of grinding, they still feel unprepared — because random practice without a coding interview study plan doesn't build reliable pattern recognition.

A structured approach changes the outcome. When you learn patterns in a deliberate sequence, each new concept builds on the last. You finish Week 8 with a mental library of reusable approaches, not a pile of half-remembered solutions.

This 8-week plan is designed for anyone with basic programming knowledge. You don't need a CS degree or prior interview experience — just consistent effort and the right sequence. By the end, you'll recognize the dominant patterns behind 80% of LeetCode problems.

The first two weeks are about building your foundation. Arrays and strings appear in nearly every coding interview, and hash maps are the secret to turning O(n²) brute-force solutions into elegant O(n) ones.

Start with basic array manipulation: traversal, in-place modifications, prefix sums, and two-pass patterns. Then shift to strings: reversals, character frequency counts, and anagram detection. These problems feel simple, but they're the building blocks for everything that follows.

Introduce complexity analysis at the same time. You should be able to explain why a nested loop is O(n²) and why a hash map lookup is O(1). Interviewers expect you to talk about time and space trade-offs, not just produce working code.

Weeks 3 and 4 introduce the four patterns that cover a huge slice of interview problems. Two pointers and sliding window handle most subarray and substring questions. Binary search is the go-to tool any time the input is sorted or you're searching a monotonic space. Stack problems include valid parentheses, next greater element, and monotonic stack variants.

Two pointers use a left and right pointer that move toward each other or in the same direction. The key trigger is a sorted array or a problem asking you to find pairs/triplets. Sliding window is used when the problem asks for the maximum or minimum subarray or substring satisfying a condition — you expand the right boundary and shrink the left when the window becomes invalid.

Binary search is often misunderstood as only useful for sorted arrays. In practice, it works on any problem where you can define a monotonic predicate — "is X feasible?" — and binary search the answer space. This unlocks a whole category of medium and hard problems.

Stack problems rely on the LIFO property to track pending context. Whenever you need to "remember the most recent thing" while iterating, reach for a stack.

Trees and graphs appear in roughly 35% of coding interviews. Week 5 focuses on binary trees: recursive DFS for path problems, iterative BFS for level-order traversal, and the standard traversal orders (inorder, preorder, postorder). Most tree problems have clean recursive solutions — practice identifying the base case and the recursive step.

Week 6 adds graphs. Graphs are just generalized trees — nodes connected by edges, with potential cycles. You'll need two core templates: DFS with a visited set (for connected components, cycle detection) and BFS with a queue (for shortest path, level-by-level exploration). The adjacency list representation is the most common format you'll encounter.

Heaps (priority queues) round out Week 6. Any time a problem asks for the k-th largest/smallest element, or requires processing items in priority order, a heap is the right data structure. Python's heapq, Java's PriorityQueue, and JavaScript's sorted structures all implement this pattern.

The final two weeks cover the patterns that separate good candidates from great ones. Dynamic programming is the most feared topic in coding interviews — but it becomes manageable once you internalize the framework: identify the state, write the recurrence relation, and choose between memoization (top-down) and tabulation (bottom-up).

Backtracking is structured recursion for generating all valid combinations, permutations, or subsets. The template is always the same: make a choice, recurse, undo the choice. It's used for problems like N-Queens, word search, and Sudoku. Pruning invalid branches early is what separates a naive backtrack from an efficient one.

Greedy algorithms make the locally optimal choice at each step. They're not always correct, but when they are, they're fast and elegant. The classic interview greedy problems include jump game (#55), task scheduler (#621), and meeting rooms (#252). Intervals problems — merge intervals, insert interval, non-overlapping intervals — use a sort-then-sweep approach that is almost always greedy.

Learning new patterns is only half the battle. The other half is retaining what you've already covered. Spaced repetition is a study technique where you review material at increasing intervals — right before you're about to forget it. Research consistently shows it outperforms re-reading or massed practice for long-term retention.

A practical schedule: after solving a problem, review it the next day, then 3 days later, then a week later. If you can explain the approach fluently at each interval, increase the gap. If you struggle, reset to the beginning. YeetCode's flashcard system tracks this automatically — you just flip cards and rate your confidence.

Concretely: while learning Week 3 patterns, spend 20% of each session reviewing Week 1–2 problems. When you hit Week 5, cycle back through Weeks 1–4. This keeps early patterns fresh while new ones accumulate.

In the final week before your interviews, shift from learning to simulating. Do timed practice sessions: 45 minutes per problem, just like a real interview. Use platforms that record your screen and audio so you can review how you explain your thought process — clear communication is half of what interviewers evaluate.

Time management in the interview matters. Spend the first 5 minutes clarifying requirements and discussing your approach before writing any code. Interviewers want to see your problem-solving process, not just the final answer. If you're stuck, verbalize your thinking — "I'm considering a sliding window here because the problem involves a contiguous subarray" shows pattern recognition even if your code isn't perfect yet.

Finally, manage your mindset. Coding interviews are stressful by design, but remember that most interviewers are rooting for you. A structured leetcode study plan like this one means you've already done the hard work. Trust the preparation, stay curious, and approach each problem like a puzzle to explore — not a test to pass.