Data Structures & Algorithms
A complete 25-lesson Data Structures & Algorithms course in Python — from Big-O and recursion through linked lists, hash tables, trees, sorting, graphs, and dynamic programming, culminating in an LRU cache, a graph pathfinder, and a Trie-based autocomplete engine.
25 Lessons · 750 min · Learning Platform Editorial Team
What you need on the desk
- Python 3 (any recent version)
- A code editor (VS Code recommended)
- No external services, accounts, or packages beyond the Python standard library required
Know this much before you start
- Comfort with basic Python syntax — variables, functions, loops, lists, and dicts
- No prior data structures or algorithms experience needed
- No advanced math background required — complexity analysis is taught from first principles
- Comfort using a terminal to run Python scripts
By the end, you can
- Analyze any function's time and space complexity using Big-O and explain the reasoning behind it
- Choose the right linear structure (array, linked list, stack, queue) for a given access pattern
- Use hash tables and trees confidently, and explain when a BST degrades to O(n)
- Implement and compare sorting algorithms, binary search, and heaps/priority queues
- Traverse graphs with BFS/DFS and find shortest paths with Dijkstra's algorithm
- Recognize overlapping subproblems and apply dynamic programming, and build real projects — an LRU cache, a graph pathfinder, and a Trie autocomplete engine