1. DSA
1.1. Watch
1.1.1. Coursera - Algorithms Part 1
1.1.1.1. Coursera - Algorithms Part 2
1.1.1.2. Bro Code - Learn DSA for Free
1.2. Practice
1.2.1. Algomonster Exercises
1.2.1.1. LeetCode (Interview Handbook Roadmap)
1.2.1.2. Hackerrank
1.3. Read
1.3.1. Algomonster Modules
1.3.1.1. Geeksforgeeks - Top 10 Algorithms in Interview Questions
1.3.1.1.1. Hands-On Data Structures and Algorithms with Python (Packtpub free)
1.3.1.1.2. Grokking Algorithms
1.3.1.1.3. Cracking the Coding Interview
1.3.1.1.4. Elements of Programming Interviews
2. OOP
2.1. Read
2.1.1. Design Patterns GOF
2.2. Practice
2.2.1. Real World Apps
2.2.1.1. Weekly Coding Challenge (John Cricket)
3. System Design
3.1. Watch
3.1.1. Educative - Grokking Modern System Design Interview
3.1.1.1. Youtube - codeKarle
3.2. Read
3.2.1. Architecture Patterns with Python
3.2.2. System Design Interview, Vol 1, Alex Xu
3.2.2.1. System Design Interview, Vol 2, Alex Xu
3.2.3. Designing Data Intensive Applications
4. Common Concepts
4.1. Read
4.1.1. Design
4.1.1.1. DDD
4.1.1.1.1. Implementing Domain-Driven Design, Vaughn Vernon
4.1.1.2. Architecture
4.1.1.2.1. Clean Architecture
4.1.2. Microservices
4.1.2.1. Microservices Patterns, Chris Richardson
4.1.2.1.1. Practical Microservices, Garofolo
4.1.3. Papers
4.1.3.1. Dynamo: Amazon’s Highly Available Key-value Store
4.1.3.2. Kafka: a Distributed Messaging System for Log Processing
4.1.3.3. Communicating Sequential Processes
4.1.3.4. Scaling Memcache at Facebook
4.1.3.5. Google Spanner
4.1.3.6. Google File System
4.1.3.7. Google Big Table
4.1.3.8. Google MapReduce
4.1.4. Compilers
4.1.4.1. Compilers: Principles, Techniques, and Tools
4.1.5. SQL
4.1.5.1. TBD
5. AI
5.1. Practice
5.1.1. Kaggle
5.1.2. Personal Project Portfolio
5.2. Watch
5.2.1. Mathematics for Machine Learning Specialization by Imperial College London (Coursera)
5.2.2. Python for Data Science and Machine Learning Bootcamp by Jose Portilla (Udemy)
5.2.2.1. Machine Learning A-Z™: Hands-On Python & R In Data Science by Kirill Eremenko and Hadelin de Ponteves (Udemy)
5.2.2.1.1. Deep Learning Specialization by Andrew Ng (Coursera)
5.2.2.1.2. Deep Learning A-Z™: Hands-On Artificial Neural Networks by Kirill Eremenko and Hadelin de Ponteves (Udemy)
5.3. Read
5.3.1. "Linear Algebra and Its Applications" by Gilbert Strang
5.3.2. "Introduction to the Theory of Statistics" by Mood, Graybill, and Boes
5.3.3. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
5.3.3.1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
5.3.3.2. "Neural Networks and Deep Learning" by Michael Nielsen