Easy Explanations + Practice Links
Why DSA Matters for SDETs
As an SDET, you write code every day—whether it’s API tests, mobile-app checks, or data validations. Understanding Data Structures and Algorithms helps you:
- Write faster, cleaner tests
- Handle big data sets without slowdowns
- Impress interviewers with solid problem-solving
Below is a gentle introduction to the key topics, with one LeetCode link per topic so you can practice right away.
1. Data Structures
These are ways to store and organize data. Pick the right one to make your code faster.
| Structure | What It Is | LeetCode Sample |
|---|---|---|
| Arrays | A list of items in order, like [1,2,3]. Fast to access by index, slow to insert in middle. | Two Sum |
| Strings | A sequence of characters, like "hello". Think of it as an array of chars. | Longest Substring Without Repeating Characters |
| Linked Lists | Each item (“node”) points to the next. Good for insert/delete, no quick random access. | Add Two Numbers |
| Stacks & Queues | Stack = LIFO (push/pop), Queue = FIFO (enqueue/dequeue). | Valid Parentheses |
| Hash Tables | Key→value map. Very fast lookup/insert (O(1) on average). | LRU Cache |
| Matrix (2D Array) | Grid of items. Useful for board games or dynamic-programming tables. | Number of Islands |
| Trees | Hierarchy of nodes. Root at top, children below. Great for nested data. | Binary Tree Inorder Traversal |
| Binary Search Trees | A sorted tree: left < root < right. Fast search if balanced. | Validate Binary Search Tree |
| Heaps | A special tree for quickly finding min or max. Used in priority queues. | Top K Frequent Elements |
| Graphs | Nodes connected by edges. Can be directed or undirected. | Course Schedule (Topological Sort) |
| Trie | Prefix tree for strings. Fast “starts with” queries. | Implement Trie (Prefix Tree) |
| Union-Find (DSU) | Keep track of connected components. Great for network/connectivity problems. | Number of Connected Components in an Undirected Graph |
2. Algorithms
These are step-by-step recipes to solve problems using data structures.
2.1 Sorting
- What it does: Rearranges items in order (e.g. smallest→largest).
- Why it matters: Many problems need sorted data.
- Practice: Sort Colors
2.2 Searching
- Binary Search: Fast lookup in sorted arrays (O(log n)).
- Practice: Binary Search
- Graph Traversals:
- DFS: Go deep, one branch at a time.
- BFS: Go wide, level by level.
- Practice: Number of Islands
2.3 Greedy & Dynamic Programming
- Greedy: Make the best local choice hoping it’s global best (e.g., interval scheduling).
- Practice: Gas Station
- Kadane’s Algorithm: Find max subarray sum in one pass.
- Practice: Maximum Subarray
- DP (Overlapping Subproblems): Cache results to avoid repeat work.
- Practice: Climbing Stairs
2.4 Shortest Paths & Trees
- Dijkstra / Bellman-Ford: Find shortest path in weighted graphs.
- Practice (Dijkstra): Network Delay Time
- Practice (Bellman-Ford style): Cheapest Flights Within K Stops
- Minimum Spanning Tree: Connect all nodes with minimal cost.
- Practice: Min Cost to Connect All Points
3. Problem-Solving Patterns
These “templates” help you recognize and solve new problems faster.
| Pattern | What It Means | Practice Link |
|---|---|---|
| Two Pointers | Use two indices moving inward or outward. | Container With Most Water |
| Sliding Window | Move a window over data to track sums/counts. | Minimum Size Subarray Sum |
| Prefix Sum | Precompute sums to answer range queries in O(1). | Subarray Sum Equals K |
| Monotonic Stack | Keep stack in increasing/decreasing order. | Daily Temperatures |
| Fast & Slow Pointers | Detect cycles or find mid-point in lists. | Linked List Cycle |
| Bit Manipulation | Use bits for flags or math hacks. | Single Number |
| Divide & Conquer | Split problem in half, solve, then merge. | Search in Rotated Sorted Array |
| Recursion | Function calls itself for similar subproblems. | Permutations |
| Backtracking | Try options, back up on failure. | N-Queens |
| Top K Elements | Use heap or selection to find K largest/smallest. | Top K Frequent Elements |
| Dynamic Programming | Build solutions from smaller solved pieces. | Climbing Stairs |
How to Use This Guide
- Pick a topic (e.g., Arrays).
- Read the simple definition above.
- Try the linked LeetCode problem.
- Review your code: ensure you know the time/space cost.
- Explain your steps out loud, as if you’re in an interview.
Ready to level up your SDET interview prep?
Practice these topics one by one, and soon you’ll tackle any DSA question with confidence!
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