Data Structures 101: The Beginner's Starter Guide

Data structures are the building blocks of every program you have ever used. This guide covers the five you will use 90% of the time — with real-world analogies and code.

Arrays

Contiguous memory, instant access by index (O(1)), slow inserts in the middle (O(n)).

Think of it as a row of numbered lockers. You know exactly which locker holds what.

Linked Lists

Nodes chained by pointers. O(1) inserts at the head, O(n) search.

Think of it as a treasure hunt where each clue points to the next. Great for queues and history — like the undo stack in your editor.

Stacks & Queues

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Hash Maps

Key-value storage with average O(1) lookups. The workhorse of modern code.

counts = {}
for item in items:
    counts[item] = counts.get(item, 0) + 1

Think of it as a dictionary — you do not flip through pages, you jump straight to the word.

Trees

Hierarchical data — think file systems or the DOM of a webpage. Binary search trees keep data sorted so search stays O(log n).

Where to practice

Start with these classic problems:

1. Two Sum (hash map)
2. Valid parentheses (stack)
3. Merge two sorted lists (linked list)
4. Invert a binary tree (recursion)

Solve them in our interactive labs, then move to LeetCode-style questions. The goal is not memorizing answers — it is recognizing which structure fits which problem.

Choosing the right one

• Random access by index → array
• Fast inserts at the front → linked list
• LIFO processing → stack
• FIFO processing → queue
• Lookup by key → hash map
• Sorted ranges & hierarchies → tree

Master these five and every interview problem starts to look familiar.