How to Study for Technical Interviews Without Burning Out: A Realistic LeetCode Plan
Published ยท Finch Team
Updated
Everyone knows they need to practice LeetCode. Almost nobody has a plan that actually works. They open the problems page, grind random mediums for a week, burn out, restart, and repeat. Months later they walk into an interview and blank on a problem they've technically "seen before."
This post is about building a study system that doesn't burn you out and actually prepares you for what shows up in real interviews.
What Technical Interviews Actually Test
Before grinding problems, understand what the interview is actually measuring:
Problem decomposition. Can you take a novel problem and break it into parts you know how to handle?
Pattern recognition. Most interview problems are variations on a small set of patterns: sliding window, two pointers, BFS/DFS, dynamic programming, binary search on answer space. Recognizing the pattern is 70% of solving the problem.
Communication. Interviewers care as much about how you think through a problem as whether you get the right answer. Talking out loud, explaining tradeoffs, asking clarifying questions: all of it is being evaluated in parallel with your code.
Time and space complexity analysis. You'll almost always be asked to analyze your solution. If you can't say "this is O(n log n) because of the sort, and O(n) space for the hash map," you're leaving points on the table.
Coding fluency. You should write clean, working code in your primary language without syntax errors, without looking up standard library methods, and without spending five minutes on basic data structure operations.
If you approach prep as "memorize solutions," you'll blank on novel problems. The actual goal is to build pattern recognition and decomposition skills. Solutions are just the vehicle.
The Realistic Time Budget
Most CS students don't have unlimited prep time. Here's what 8 weeks looks like if you're a student with coursework:
1-2 hours per day, 5 days per week = ~8-10 hours per week. Over 8 weeks that's 64-80 hours of focused practice. That's enough to be competitive for most mid-tier roles and many top-tier roles if you're intentional about what you practice.
The common failure mode: grinding hard for 3 weeks, burning out, doing nothing for 2 weeks, cramming the week before the interview. That's worse than consistent, moderate effort spread over the same period.
If you have less than 4 weeks, skip learning new topics and go straight to the review and mock interview phase with the patterns you already know. Reinforcing what you have is more valuable than half-learning something new under pressure.
The Topic Order That Builds on Itself
Don't practice randomly. Some patterns are foundational (you need them to understand other patterns) and some are high-frequency in real interviews. Here's an order that compounds:
Week 1-2: Arrays and Strings (Foundation)
Almost every interview problem involves arrays or strings in some form. Get fluent in:
- Two-pointer technique: sorted array problems, palindrome checks, pair sums
- Sliding window: longest substring, maximum subarray sum, window-based constraints
- Hash maps for frequency counting: anagram detection, most common element, two-sum variations
- Prefix sums: range query problems, subarray sum equals k
Practice 10-15 problems per pattern. Don't move on until two-pointer and sliding window feel automatic. These will come up in almost every interview.
Week 3: Trees and Graphs (High Frequency)
Tree problems show up constantly. Focus on:
- DFS and BFS (recursive and iterative versions of both)
- Binary tree level-order traversal
- Path sums, tree properties, lowest common ancestor
- Basic graph adjacency representation (adjacency list and matrix)
- Connected components and cycle detection
The template for binary tree DFS is about 15 lines. Once you have it cold, most tree problems are variations. Write it out from memory 5 times until it's automatic.
Week 4: Linked Lists and Stacks
These are formulaic enough that once you see the patterns, they become reliable interview points:
- Slow/fast pointer technique: cycle detection, middle of list, kth from end
- Reverse a linked list (iterative and recursive)
- Monotonic stack problems: next greater element, daily temperatures, histogram problems
- Stack for balanced parentheses and expression evaluation
Week 5: Binary Search and Sorted Structures
Binary search appears in far more places than "find a number in sorted array." The advanced pattern is binary searching the answer space: finding the minimum feasible value or maximum feasible allocation that satisfies a constraint. This shows up in many mediums and hards.
Practice at least 10 problems specifically in this category before your interview. It's underrepresented in early study plans and overrepresented in actual interviews.
Week 6: Dynamic Programming
DP is where students spend too much time memorizing solutions and too little time understanding the underlying pattern.
Focus on:
- 1D DP: coin change, house robber, climbing stairs, longest increasing subsequence
- 2D DP: unique paths, edit distance, longest common subsequence, knapsack
- Recognizing when a problem has overlapping subproblems and optimal substructure
DP is high-ceiling/high-floor. If you understand the core "define subproblem, identify recurrence, handle base cases" framework, you can reason about novel DP problems. If you're memorizing solutions, you'll blank on anything slightly different.
Week 7-8: Review and Mock Interviews
Stop learning new topics at week 7. Spend this time doing timed practice sets and mock interviews.
A single mock interview session is worth 10 hours of solo grinding. You cannot simulate the pressure of talking out loud while someone watches until you actually do it. Use Pramp, Interviewing.io, or ask a friend to time you and watch you code without helping.
Aim for at least 4 mock sessions in the final two weeks.
How to Practice Effectively (The Habits That Matter)
Write pseudocode before touching real code. If you can't write the algorithm in plain English first, you don't understand it well enough to code it. Force this discipline every single time.
When you're stuck, struggle for 20-30 minutes before looking at the solution. The discomfort of being stuck is where pattern recognition actually develops. Looking at the solution at the 5-minute mark teaches you the answer but not the skill.
After solving a problem, explain it out loud. Time complexity, space complexity, edge cases, what you'd do differently. Talking through a completed solution builds the interview muscle that solo grinding doesn't.
Categorize every problem you finish. Tag it with the primary pattern: "sliding window," "two pointers," "DFS on tree," "binary search on answer." After 50 tagged problems, you'll see that most mediums fall into 8-10 categories. That's the insight that makes you better at novel problems.
Aim for 70% mediums, 20% hards, 10% easy review once you're past the first week. Easy problems build fluency. Medium problems build the pattern recognition that shows up in real interviews. Hard problems build the edge case instincts that top-tier companies test.
The Problem Lists With the Best Signal-to-Noise Ratio
There are dozens of curated problem lists. These are the ones actually worth using:
Grind 75: Updated and curated version of the original Blind 75. Weighted by interview frequency and ranked by importance. If you only use one list, use this one. The problems cover the key patterns without being overwhelming.
LeetCode 75: Official LeetCode study plan, 75 problems across all major categories. Good for structured breadth-first prep if you have 6+ weeks.
NeetCode 150: Better coverage if you want more depth per topic. Neetcode's YouTube explanations are genuinely excellent for understanding the reasoning behind solutions, not just the code.
Company-specific tagged problems: LeetCode tags problems by company. If you have an interview at a specific company in 3 weeks, filtering to their tagged problems is worth doing. Patterns repeat at specific companies.
What Not to Do
Don't try to solve 500 problems. Depth beats breadth. Fully understanding 100 problems and being able to explain every solution beats having "seen" 500 problems with 20% retention.
Don't practice in a language you're not fluent in. If you're strongest in Python, practice in Python. If you're applying to Java-heavy companies and want to use Java, practice in Java for at least a month before interviewing. Switching languages mid-prep is expensive.
Don't skip system design entirely. For new grad roles, system design is usually light: a 30-45 minute open-ended question about how you'd design something at a high level. But if you've never thought about it at all, you'll flounder when asked to design a URL shortener or a rate limiter. Spend 5-10 hours reading about databases, caching, load balancers, and API design. That's enough to hold a reasonable conversation.
Don't neglect the talking-out-loud piece. At least once a week, solve a problem with the requirement that you must narrate every step as you go. Your first time doing this will feel awkward. By the fifth time it'll feel natural, which is what you need it to feel like in a live interview.
The Two Tracks Running in Parallel
Technical interview prep and the actual application process are parallel tracks. While you're practicing algorithms, applications need to be going out, because if you don't get to the interview stage, the prep doesn't matter.
The recruiting calendar closes earlier than most students expect. Top-tier companies fill spots in October-November for summer internships. See our breakdown of when to start applying for software engineering internships for the full timeline.
Getting applications out efficiently while prepping is where Finch helps. Each week it picks the 3 internships or new-grad roles that fit your major best, so less of your limited time goes to searching and more goes to actual interview prep.
8-Week Study Plan Checklist
Week 1-2: Arrays and Strings
- Two-pointer technique (15 problems)
- Sliding window (10 problems)
- Hash map patterns (10 problems)
- Prefix sums (5 problems)
Week 3: Trees and Graphs
- Binary tree DFS recursive and iterative (10 problems)
- BFS and level-order traversal (8 problems)
- Graph traversal and connected components (7 problems)
Week 4: Linked Lists and Stacks
- Slow/fast pointer problems (8 problems)
- Monotonic stack problems (8 problems)
Week 5: Binary Search
- Classic binary search (5 problems)
- Binary search on answer space (10 problems)
Week 6: Dynamic Programming
- 1D DP: coin change, house robber, climbing stairs (8 problems)
- 2D DP: unique paths, LCS, edit distance (7 problems)
Week 7-8: Review and Mock Interviews
- Complete Grind 75 or LeetCode 75
- 4+ timed mock interview sessions (out loud, with a timer)
- Review all problems you initially got wrong
- Practice explaining time and space complexity for every solution
- Tag and categorize every problem you've solved by pattern