DS

Data Structures & Algorithms

intermediate

The patterns behind every fast program.

Data structures are ways to organize data; algorithms are recipes for working with it. Together they explain why one program handles a million records instantly while another chokes on ten thousand. This track covers the core toolkit — Big-O, arrays, hash maps, stacks, queues, recursion, trees, graphs, sorting — with runnable Python examples. It's the language-agnostic layer under every language you'll ever learn, and the backbone of technical interviews.

Problem SolvingTechnical InterviewsPerformanceSystem DesignCS Fundamentals
0%
0/9 lessons

Setup

Examples use Python because it reads like pseudocode — but every concept transfers to any language. If you finished the Python track's setup you're ready. Otherwise install Python and run examples in a file or the REPL.

Bash
winget install Python.Python.3.12

# verify:
python --version

# run examples:
python dsa_practice.py

Lessons

1
Big-O: Measuring Speed
Before learning any data structure, you need the vocabulary for comparing them. …
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2
Arrays & Strings
The array is the simplest data structure: items in a row, side by side in memory…
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3
Hash Maps & Sets
The hash map is the most useful data structure in programming: look anything up …
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4
Stacks, Queues & Linked Lists
Three structures about controlling the ORDER things come out: stacks (last in, f…
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5
Recursion
A recursive function calls itself on a smaller piece of the problem until the pi…
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6
Trees & Binary Search
A tree is nodes and pointers arranged as a hierarchy: one root, branching childr…
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7
Graphs: BFS & DFS
A graph is nodes plus edges connecting them — no hierarchy required, cycles allo…
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8
Sorting: How & When
Sorting is the most-studied problem in computer science, and the ideas inside th…
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9
DSA Cheatsheet
Every structure, its costs, and when to reach for it — one page.
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