Coding Interview Cheat Sheet: Data Structures and Algorithms for Technical Interviews

Master these core data structures before any coding interview. The interviewer will test your ability to choose the right data structure for each problem and understand the time and space complexity tradeoffs.

Arrays are the most fundamental data structure. Arrays store elements in contiguous memory with O(1) access time. Know how to traverse arrays, find elements using binary search on sorted arrays, and understand array manipulation techniques. Many coding interview questions start with array problems.

Linked Lists store elements with pointers connecting each node. Unlike arrays, linked lists allow O(1) insertion and deletion at known positions. Practice problems involving linked list reversal, cycle detection using fast and slow pointers, and merging sorted linked lists. The interviewer often asks linked list questions to test your pointer manipulation skills.

Hash Tables provide O(1) average case lookup, insertion, and deletion. Use hash tables when you need to count frequencies, find duplicates, or implement caching. The two sum problem is a classic example where a hash table reduces time complexity from O(n^2) to O(n).

Stacks and Queues are linear data structures with specific access patterns. Stacks follow LIFO (last in, first out) and queues follow FIFO (first in, first out). Use stacks for matching parentheses, tracking function calls, and implementing DFS. Use queues for BFS and level-order tree traversal.

Trees are hierarchical data structures. Binary trees, binary search trees, and balanced trees appear frequently in coding interviews. Know how to traverse trees using recursion with preorder, inorder, and postorder methods. Practice problems involving tree depth, path sums, and tree construction from traversals.

Graphs model relationships between entities. Represent graphs using adjacency lists or matrices. Graph traversal algorithms like DFS and BFS are essential for coding interviews. Practice problems involving connected components, shortest paths, and cycle detection in directed and undirected graphs.

Recognizing algorithm patterns and question patterns helps you solve coding interview questions faster. Include these must-know algorithm questions in your interview cheat sheet.

Two Pointers (also called two-pointers) uses two pointers that move through an array or linked list. Use this pattern for problems involving pairs, reversing arrays, or finding subarrays. The technique often reduces time complexity from O(n^2) to O(n).

Sliding Window maintains a window that slides across data. This pattern solves substring and subarray problems efficiently. Track the window state as you expand and contract it to find optimal solutions.

Binary Search halves the search space with each comparison. Apply binary search beyond sorted arrays to any problem where you can eliminate half the possibilities. Know both iterative and recursive implementations for your coding interview.

Recursion solves problems by breaking them into smaller subproblems. Many coding interview questions have elegant recursive solutions. Understand how to write base cases and recursive cases. Be prepared to optimize recursive solutions using memoization.

Dynamic Programming (DP) optimizes recursive solutions by storing subproblem results. Identify DP problems by looking for overlapping subproblems. Build solutions bottom-up using tabulation or top-down using memoization with recursion.

Backtracking explores all possible solutions by building candidates incrementally. Use backtracking for permutations, combinations, and constraint satisfaction problems. Prune invalid paths early to optimize your solution.

Every coding interview requires you to analyze time complexity and space complexity. Include this complexity reference in your cheat sheet for coding interviews.

Common time complexities from best to worst: O(1) constant, O(log n) logarithmic, O(n) linear, O(n log n) linearithmic, O(n^2) quadratic, O(2^n) exponential. Know which data structure operations and algorithms fall into each category.

Binary search is O(log n). Merge sort and quicksort average O(n log n). Hash table operations average O(1). Graph traversal is O(V + E). Understanding these complexities helps you discuss tradeoffs with the interviewer.

Space complexity matters too. Recursive solutions use stack space proportional to recursion depth. DP solutions may use more memory to save computation time. Always mention the space complexity tradeoff in your technical interview.

Sorting algorithms appear directly and indirectly in coding interviews. Know these for your technical interview cheat sheet.

Merge sort runs in O(n log n) time and is stable. It divides the array recursively, sorts each half, then merges. Merge sort uses O(n) extra space but guarantees worst-case performance.

Quicksort averages O(n log n) but can degrade to O(n^2) in the worst-case scenario. Choose a good pivot to avoid worst-case performance. Quicksort sorts in place, using O(log n) stack space for recursion.

Know when to use built-in sorting versus implementing your own. Many coding problems become easier once data is sorted. Sorting first can reduce overall time complexity.

Graph algorithms solve real interview questions. Include graph traversal techniques in your coding interview cheat sheet.

DFS (Depth-First Search) explores as deep as possible before backtracking. Implement DFS iteratively with a stack or recursively. Use DFS for path finding, cycle detection, and topological sorting.

BFS (Breadth-First Search) explores level by level. Use a queue for BFS implementation. BFS finds shortest paths in unweighted graphs and solves problems requiring level-order processing.

Know how to detect cycles in directed and undirected graphs. Track visited nodes and nodes currently in the recursion stack for directed graph cycle detection. For undirected graphs, track visited nodes and parent nodes.

Use this cheat sheet effectively in your interview preparation. Here are best coding practices before your technical interview.

Practice on LeetCode and similar platforms daily. Focus on LeetCode's problems that match patterns rather than memorizing solutions. Do mock interviews to simulate real interview conditions under time constraints. Mock interviews reveal weaknesses and build confidence. Many coding questions become easier when you practice with coding practices that work.

Review your cheat sheet the night before. Don't cram new material. Instead, review core concepts and algorithm patterns you've already practiced. Get good sleep so you can think clearly during the coding interview.

Know the shortcuts in your coding language. Python, Java, or whatever language you choose, know the standard library functions for common operations. This saves time during the actual interview.

Follow this step-by-step approach during your tech interview. Your ability to communicate matters as much as solving the problem. Some companies also include system design rounds, so this cheat sheet for coding interviews focuses on the algorithm portion.

First, clarify the problem with the interviewer. Ask about input constraints, edge cases, and expected output. Don't start coding until you understand the problem completely. Many coding mistakes come from misunderstanding requirements.

Second, discuss your approach before writing code. Explain your algorithm, data structure choices, and time complexity analysis. The interviewer wants to see your thought process. This is not cutting corners in your code but showing deliberate problem-solving.

Third, write clean code with meaningful variable names. Test your code with examples and edge cases. Walk through corner cases like empty inputs, single elements, and boundary conditions. Debug systematically if something goes wrong.

Avoid these mistakes that cost candidates coding interview offers.

Don't jump into coding without planning. Spending two minutes thinking saves ten minutes debugging. Optimize after you have a working solution, not before. Get the brute force solution working first.

Don't ignore edge cases. Test cases with empty inputs, nulls, and boundary conditions often break solutions. The interviewer specifically looks for edge case handling.

Don't forget to analyze complexity. Always state your time complexities and space complexity after explaining your solution. Show the interviewer you understand algorithm efficiency and can discuss tradeoffs.

Here's something no cheat sheet tells you. The engineers who get the best offers aren't just well-prepared technically. They walk into interviews with credibility already established.

Think about two candidates who both memorized this cheat sheet. One applies cold with a solid resume. The other has built a personal brand through blog posts, open source contributions, and community involvement. The hiring manager already knows their name. Who gets more leeway when they struggle on a problem? Who negotiates the higher compensation?

Personal branding is a multiplier for your technical skills. You could master every algorithm and data structure, but if nobody knows you exist, that knowledge doesn't create opportunities. Start building your visibility now. Write about what you're learning. Share your cheat sheets publicly. Help others preparing for interviews. The compound effect of consistent visibility transforms your career trajectory over time.

Save this coding interview cheat sheet for your interview prep. Review it regularly, not just the night before your interview. Each time you practice LeetCode problems, connect them back to patterns in this cheat sheet.

Schedule mock interviews with friends or use platforms like Pramp. Real interviews feel different from solo practice. Get feedback on your ability to communicate your solutions clearly.

Build your own cheat sheet with notes from problems you've solved. Personal notes stick better than generic references. Add examples from many coding problems you've encountered. This becomes your secret weapon for tech interview success.

What is a coding interview cheat sheet? A coding interview cheat sheet is a quick reference guide covering essential data structures, algorithms, and coding patterns for technical interviews. It helps you review key concepts before and during your interview preparation.

What should I include in my interview cheat sheet? Include data structures like arrays, linked lists, hash tables, trees, and graphs. Add algorithm patterns like two pointers, sliding window, binary search, recursion, and dynamic programming. Include time and space complexity references for common operations.

How should I use a cheat sheet for coding interviews? Use the cheat sheet for regular review during preparation, not as something to memorize the night before. Connect each problem you practice to patterns in the cheat sheet. Build your own notes alongside the reference material.

Do I need LeetCode Premium for interview prep? LeetCode Premium helps but isn't required. Free LeetCode problems cover most essential patterns for all relevant DSA types. Focus on understanding algorithm concepts rather than completing specific problem counts. Quality practice beats quantity.