Subject
6 entries
Curriculum
Bookmarks
OSSU Data Science Curriculum
OSSU Data Science is a free, community-curated curriculum for self-teaching data science to the equivalent of a university degree — structured sequence from linear algebra and statistics through machine learning and specialization. A roadmap for going deep without a formal program.
A Practical Intro to Data Science — Zipfian Academy
Zipfian Academy's canonical post on what data science actually involves in practice — widely shared as a curriculum reference and one of the clearest articulations of the data scientist skill set in 2014. Clare Corthell called it 'still one of the best posts on the topic.'
Self-Study Guide to Machine Learning
Machine Learning Mastery's self-study guide to machine learning — Jason Brownlee's staged curriculum for practitioners who want to learn ML without a formal background. Prescriptive and pragmatic: pick an algorithm, implement it, run it on data, iterate.
Metacademy — Bayesian Machine Learning Roadmap
Roger Grosse's curated Metacademy roadmap for learning Bayesian machine learning from scratch — a sequenced path through Bayesian inference, probabilistic graphical models, approximate inference, and nonparametric methods. The systematic route through a complex prerequisite landscape.
Metacademy
Metacademy is a 'web of knowledge' for machine learning — a dependency graph of concepts where each node links to learning resources and its prerequisites. The idea: show exactly what you need to know before you can understand any given topic.
What Every Computer Science Major Should Know
Matt Might's canonical essay on what a CS graduate should know across breadth and depth — from formal theory to systems to software engineering practice. A widely-shared framework for thinking about CS education and self-directed learning gaps.