Data Structures & Algorithms (DSA) are the foundation of technical interviews and efficient software — and also one of the most intimidating topics to start learning. This page is your starting point: a structured path through our DSA series, in the order we recommend reading them, from the very first concept (Big O) through to sorting algorithms.
Each guide is written to be beginner-friendly, with real-world analogies and working JavaScript code — no dense math notation required to get started.
1. Fundamentals
Start here. Before comparing data structures or algorithms, you need a way to talk about how "fast" or "slow" they are.
Big O Notation Explained: A Beginner's Guide to Time & Space Complexity
What Big O actually measures, the complexities you'll see most often (O(1),O(log n),O(n),O(n²)...), and why it matters before you touch a single algorithm.
2. Data Structures
How data is actually organized in memory, and the trade-offs that come with each choice.
Arrays vs Linked Lists: What's the Difference and When to Use Each
Contiguous memory vs pointer-linked nodes, and why one trades fast lookups for fast inserts.Stacks and Queues Explained with Real-Life Examples
LIFO vs FIFO, explained through plates and coffee-shop lines, with workingpush/popandenqueue/dequeuecode.
3. Algorithms
Now that the building blocks make sense, here's how to actually search and sort through them efficiently.
Binary Search Explained Step by Step (with JavaScript Code)
How cutting a sorted list in half repeatedly turns anO(n)search intoO(log n).Sorting Algorithms Explained: Bubble Sort, Merge Sort & Quick Sort
The three sorting algorithms you'll actually get asked about, with complexity trade-offs and code for each.
What's Next
This series is growing — trees, graphs, recursion, and dynamic programming are next on the list. New DSA posts are tagged with the DSA label, so you can always find the latest additions there, or just bookmark this page and check back.
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