Computer Science Learning Path

Learn Vim, command line basics, and shell scripting

Master Git for project management and collaboration

Learn information retrieval techniques, search engines, and data indexing

Learn container technology for modern development

Essential math for algorithms and machine learning

Logic, set theory, graph theory, and combinatorics

Foundation for machine learning and algorithms

Entropy, coding, and communication theory

Start with Python or C - Harvard CS50, MIT 6.100L

UCB CS61B, Princeton Algorithms - Core CS foundation

MIT 6.031, UCB CS169 - Write production-quality code

Stanford CS106B/X, MIT 6.824 - Systems programming

Nand2Tetris, UCB CS61C - Build a computer from scratch

MIT 6.S081, UCB CS162 - Write your own OS kernel

Stanford CS144 - Implement TCP/IP protocol stack

CMU 15-445, UCB CS186 - Build your own database

UCB CS170, MIT 6.046 - Advanced algorithm techniques

MIT 6.045J - Automata, computability, complexity

Stanford CS255 - Mathematical foundations of security

Stanford EE364A - Optimization in ML and algorithms

Andrew Ng ML, Stanford CS229 - Core ML concepts

Stanford CS231n, CS224n - CNNs, RNNs, Transformers

UCB CS285 - Deep RL and policy optimization

CMU 10-414 - Deep learning systems and optimization

Stanford CS148, Games101 - Rendering and visualization

CMU 15-418/Stanford CS149 - GPU programming and CUDA

MIT 6.824 - Consensus, replication, fault tolerance

UCB CS161, SU SEED Labs - Security and cryptography