Data Structures and Algorithms Interview Prep: Essential Guide

Arrays and Strings are the foundation. Master in-place manipulation, two-pointer technique, sliding window, prefix sums, and string matching. These appear in over 40% of coding interviews.

Linked Lists test pointer manipulation skills. Practice reversal, cycle detection (Floyd's algorithm), merge operations, and finding intersection points. Understand when linked lists are preferred over arrays.

Stacks and Queues are essential for expression evaluation, BFS/DFS, and monotonic stack/queue problems. Know how to implement them and recognize problems where they apply (next greater element, valid parentheses, sliding window maximum).

Trees and Graphs are the most complex and frequently tested topics. For trees, master traversals, BST operations, lowest common ancestor, and tree DP. For graphs, master BFS, DFS, shortest path algorithms (Dijkstra, Bellman-Ford), topological sort, and union-find.

Pattern recognition is the key to solving unfamiliar problems. Here are the patterns that cover 90% of interview problems.

Sliding Window: Used when you need to find a subarray or substring satisfying some condition. Fixed-size windows for problems like maximum sum subarray of size K. Variable-size windows for longest substring without repeating characters.

Dynamic Programming: Break problems into overlapping subproblems. Start with 1D DP (climbing stairs, house robber), then 2D DP (longest common subsequence, edit distance), then advanced (knapsack variations, interval DP). Always identify the state, recurrence relation, and base case.

Binary Search: Applies beyond simple sorted array search. Use for finding boundaries (first/last occurrence), search in rotated arrays, and optimization problems (minimum/maximum satisfying a condition).

Backtracking: For combinatorial problems like permutations, combinations, subsets, N-Queens, and Sudoku solver. The key insight is to explore all possibilities while pruning invalid paths early.

Step 1: Understand the problem completely. Restate it in your own words, clarify input format, constraints, and edge cases. Ask about expected time/space complexity.

Step 2: Work through examples. Use the given examples and create your own, especially edge cases (empty input, single element, maximum size). This often reveals patterns.

Step 3: Identify the pattern and plan. Based on the problem characteristics, identify which algorithm pattern applies. State your approach before coding: 'This looks like a sliding window problem because we need to find a contiguous subarray satisfying condition X.'

Step 4: Code the solution. Write clean, readable code with meaningful variable names. Handle edge cases. For interviews, do not worry about perfect code style, but ensure correctness and clarity.

Step 5: Test and analyze. Walk through your code with an example. State the time and space complexity. Discuss potential optimizations if the interviewer asks.

Weeks 1-2: Arrays, Strings, and Hash Maps. Solve 30 problems covering two pointers, sliding window, prefix sums, and hash map patterns. Focus on LeetCode easy and medium.

Weeks 3-4: Linked Lists, Stacks, Queues, and Trees. Solve 30 problems covering list manipulation, monotonic stacks, BFS/DFS traversals, and BST operations.

Weeks 5-6: Graphs and Dynamic Programming. These are the hardest topics and deserve dedicated time. Solve 30 problems covering BFS/DFS, shortest paths, topological sort, 1D/2D DP, and common DP patterns.

Weeks 7-8: Mixed practice and weak area review. Solve 20 mixed problems without knowing the topic in advance. Review all problems you struggled with. Do timed practice (2 problems in 60 minutes).

Throughout all weeks, maintain a problem journal noting the approach, key insight, and mistakes for each problem. Review this journal regularly.