You open the recruiter's email. The on-site is scheduled. You count the weeks on your calendar and land on a number: twelve. Twelve weeks to go from wherever you are right now to passing a FAANG coding interview. The real question behind every 90 day FAANG preparation plan isn't whether the timeline is enough. It's whether you'll spend those twelve weeks in the right order.

Why Most 90 Day Plans Fail

A 90-day FAANG preparation schedule sounds reasonable. Three months, focused study, a clear deadline. But most engineers who follow one still underperform.

The failure mode is almost always the same: they study topics in the wrong order. A typical plan might start with "easy problems for two weeks, then mediums, then hards." Or it might group by popularity: arrays and strings first because they're common, then trees, then DP at the end because it's hard. Both approaches miss the same thing. DSA topics have dependencies.

You can't reason about binary tree traversal without understanding recursion. You can't build recursive intuition without understanding how the call stack works. And you can't use a hash table for pattern problems until you've internalized the data structure's lookup mechanics. Most engineers discover these gaps mid-plan. They hit binary trees in Week 5 and realize their recursion understanding is shaky. They attempt a DP problem in Week 9 and can't formulate the recurrence because they never properly learned the recursive substructure. Each gap sends them backwards, and a 12-week plan quietly becomes a 6-week plan with 6 weeks of rework.

Whether rigid learning paths help or hurt motivation probably depends on the person. But the dependency problem is upstream of that debate. Whatever schedule you follow, the ordering has to respect prerequisites.

So: sequence your preparation by dependency order, not by difficulty or topic popularity. Every concept you study should build directly on what you studied the week before.

The Week by Week FAANG Preparation Roadmap

This roadmap follows a dependency order: Arrays, Linked Lists, Hash Tables, Stacks, Queues, Trees, Heaps, Recursion, Graphs, Backtracking, Sorting, Searching, Dynamic Programming. Each week's material assumes the previous weeks are solid.

The order matters concretely. Take Coin Change, a standard DP interview problem. To solve it, you need to formulate a recurrence relation (requires recursion), identify overlapping subproblems (requires understanding multiple recursive calls), and optimize with memoization (requires understanding how cached results map to subproblem states). An engineer who jumps to DP in Week 3 will memorize the solution. An engineer who reaches DP in Week 11, after building recursive intuition through eight weeks of prerequisite work, will derive the solution. That's what the interview actually tests.

When Your Preparation Should Add Pressure

Most engineers practice in comfort conditions for 11 weeks and then panic in their first mock interview. The gap between "I solved this at my desk with no timer" and "I solved this in 20 minutes with no hints" is wider than most people expect. The right time to introduce timed practice is after foundations are solid but before advanced topics begin. That's around Week 6.

By Week 6, you've covered arrays, linked lists, hash tables, stacks, queues, and started binary trees. That's enough pattern coverage to attempt problems under realistic constraints. You won't know every pattern yet, but you know enough to practice the skill of solving under pressure separately from the skill of learning new patterns.

In Weeks 6-8, start attempting previously completed problems under timed conditions. Set a timer matching interview constraints, 20 minutes for mediums and 30 minutes for hards. No hints, no looking back at your notes. The goal isn't to pass every attempt. You're training the feeling of working without a safety net while the material is still fresh.

By Weeks 9-12, add full assessment practice. Attempt multi-problem sets under a 50-minute timer, the way actual interview rounds work. By this point you've covered enough of the learning path to simulate realistic interview coverage.

The spacing matters here. Research on the spacing effect shows that practice distributed across weeks produces stronger retention than the same volume crammed into the final days. Starting pressure practice at Week 6 gives you six full weeks of spaced, timed practice before the interview, compared to the single mock interview most people cram the weekend before their on-site.

How to Know You're On Track

Vague progress signals ("I feel like I'm getting better") aren't reliable under stress. You need concrete checkpoints at each month boundary. If three or more items at any checkpoint don't describe you, don't move to the next month. Spend an additional week on the gaps. A 13-week plan that's solid beats a 12-week plan with holes.

Month 1 (Week 4)

By Week 8

Final checkpoint (Week 12)

Going Deeper

This roadmap gives you the order. The rest is depth. For the complete strategy behind each topic, including how to prove correctness, when to use each pattern, and how to build the identification skill that separates pattern matchers from algorithm thinkers, see our FAANG coding interview preparation guide. For a deeper look at how dependency-ordered learning applies specifically to recursion and dynamic programming, those guides walk through that dependency order in detail.

The Arrays and Singly Linked List courses on Codeintuition map directly to Weeks 1-2 of this roadmap, and they're permanently free. They cover two pointers, sliding window, fast/slow pointers, and interval merging, the foundation patterns that every subsequent week in this roadmap builds on. No trial, no expiration.

Think about Coin Change from the Week 11 DP module. To derive that recurrence, you needed recursion (Week 7), which needed stack mechanics (Week 4), which needed array fundamentals (Week 1). An engineer who jumped to DP in Week 3 would memorize the solution. An engineer who reached it in Week 11, after building each prerequisite layer, would construct it from the invariant. That's the difference dependency order produces. Not more knowledge, but knowledge that holds together under pressure because every piece supports the one above it.