Finish every path in a Program, then pass a blind review of your final project to earn Honors.
Computer Science
The full OSSU computer science course map, free from start to finish, with real work confirmed at every step.
- CS Curriculum 1: Intro CS
- CS Curriculum 2: Core programming
- CS Curriculum 3: Core math
- CS Curriculum 4: CS tools
- CS Curriculum 5: Core systems
- CS Curriculum 6: Core theory
- CS Curriculum 7: Core security
- CS Curriculum 8: Core applications
- CS Curriculum 9: Core ethics
- CS Curriculum 10: Final project
67 milestones · About 2 years at about 20 hours a week: 1698 hours of course time, plus your projects.
How this works
- Take each free course.
- Pass its free self-check.
- Show your work as a proof. People who saw the work confirm it.
- Finish every path, then pass a blind review of your final project to earn Honors.
Related paths
Optional practice. Not needed for Honors.
Modelled on a typical 4-year undergraduate program. Course map adapted from OSSU Computer Science (MIT License)
Data Science
The full OSSU data science course map, free from start to finish, with real work confirmed at every step.
- DS Curriculum 1: Intro to Data Science
- DS Curriculum 2: Intro to CS
- DS Curriculum 3: Data structures and algorithms
- DS Curriculum 4: Databases
- DS Curriculum 5: Single-variable calculus
- DS Curriculum 6: Linear algebra
- DS Curriculum 7: Multivariable calculus
- DS Curriculum 8: Statistics and probability
- DS Curriculum 9: Data science tools and methods
- DS Curriculum 10: Machine learning and data mining
- DS Curriculum 11: Final project
74 milestones · About 2 years at about 20 hours a week: 2038 hours of course time, plus your projects.
How this works
- Take each free course.
- Pass its free self-check.
- Show your work as a proof. People who saw the work confirm it.
- Courses you already finished in the Computer Science curriculum count here too.
- Finish every path, then pass a blind review of your final project to earn Honors.
Related paths
Optional practice. Not needed for Honors.
Modelled on a typical 4-year undergraduate program. Course map adapted from OSSU Data Science (CC BY-SA 4.0)
AI Engineering
All 20 phases of AI Engineering from Scratch, free, from maths to production and safety, with real work checked at every phase.
- AI Engineering 0: Setup & Tooling
- AI Engineering 1: Math Foundations
- AI Engineering 2: ML Fundamentals
- AI Engineering 3: Deep Learning Core
- AI Engineering 4: Computer Vision
- AI Engineering 5: NLP: Foundations to Advanced
- AI Engineering 6: Speech & Audio
- AI Engineering 7: Transformers Deep Dive
- AI Engineering 8: Generative AI
- AI Engineering 9: Reinforcement Learning
- AI Engineering 10: LLMs from Scratch
- AI Engineering 11: LLM Engineering
- AI Engineering 12: Multimodal AI
- AI Engineering 13: Tools & Protocols
- AI Engineering 14: Agent Engineering
- AI Engineering 15: Autonomous Systems
- AI Engineering 16: Multi-Agent & Swarms
- AI Engineering 17: Infrastructure & Production
- AI Engineering 18: Ethics, Safety & Alignment
- AI Engineering 19: Capstone Projects
- AI Engineering 20: Capstone
48 milestones · About 306 hours of lessons (the curriculum's own estimate), plus your projects and capstone.
Before you start
Best after the maths and data science foundations. Not needed for Honors.
How this works
- Work through each phase's free lessons and pass their self-checks.
- Show the phase's work and one project of your own as proofs. People who saw the work confirm each.
- Outcomes you finished in other Varsity curricula count here too.
- Finish every phase, then pass a blind review of your capstone system to earn Honors.
Related paths
Optional practice. Not needed for Honors.
Modelled on a full AI engineering curriculum: maths first, through to production and safety. Course map adapted from AI Engineering from Scratch (MIT License)
AI Research
Six AI Engineering phases, four research projects and a paper you reproduce and extend, free, with real work checked at every step.
- AI Engineering 1: Math Foundations
- AI Engineering 3: Deep Learning Core
- AI Engineering 7: Transformers Deep Dive
- AI Engineering 9: Reinforcement Learning
- AI Engineering 10: LLMs from Scratch
- AI Engineering 17: Infrastructure & Production
- AI Research 1: Backprop from scratch
- AI Research 2: Small GPT and loss curves
- AI Research 3: LoRA fine-tune and published evaluation
- AI Research 4: PPO or GRPO on a toy task
- AI Research 5: Capstone
22 milestones · About 36 weeks for the research paths and capstone, after the six AI Engineering phases.
Before you start
Best after the maths foundations. Not needed for Honors.
How this works
- Do the six AI Engineering phases it builds on (they count in both curricula).
- Do each research project in a public repo, with tests or an evaluation and a short write-up.
- People who know the topic confirm each step.
- Reproduce and extend one paper, then pass a blind review of it to earn Honors.
Related paths
Optional practice. Not needed for Honors.
Modelled on the research track of a graduate programme: build it, test it, write it up, defend it.