01
Asymptotic analysis
How do I compare two algorithms without running them?
CourseFree
6.006 Introduction to Algorithms ↗MIT OCW
Erik Demaine. Full lectures, problem sets and solutions.
Core · Module 03
Data Structures & Algorithms
The core intellectual content of computer science: how to organise data so that questions about it become cheap.
By the end of this module
You can choose the right structure for a problem and justify the choice with a complexity argument.
0%0 / 6 complete
02
Arrays, lists, trees, heaps, hash tables
What question is each structure optimised to answer quickly?
InteractiveFree
VisuAlgo — algorithm and data structure visualisations ↗NUS
Step through every structure and algorithm, one operation at a time.
03
Sorting, searching, divide and conquer
Why can't comparison sorting beat n log n?
04
Graph algorithms
How many real problems are secretly graph problems?
05
Dynamic programming and greedy algorithms
When is it safe to commit to a local choice?
Check yourself
End-of-module quiz
Every answer comes with the reasoning, not just a verdict. Getting one wrong and reading why is the point.
01Hash table lookup is O(1) on average but O(n) in the worst case because:
02Comparison-based sorting cannot beat O(n log n) because:
03Dynamic programming applies when a problem has:
Answer all 3 to finish the module.