Distributed Systems & Concurrency Patterns is a deep technical course designed to prepare learners to build scalable, fault-tolerant systems that operate reliably across multiple nodes and threads. The course starts by introducing the principles of distributed computing, including consistency, availability, and partition tolerance (CAP theorem). Learners explore key architectural components such as nodes, messaging systems, consensus algorithms (Paxos, Raft), leader election, and distributed state management. The course covers concurrency fundamentals—threads, locks, race conditions, deadlocks, and thread-safe data structures—before transitioning to concurrency patterns such as producer-consumer, reader-writer, and thread pools. Technologies like Java Concurrency API, Akka (actor model), Go channels, and Python asyncio are used for hands-on practice. Learners study distributed coordination with tools like ZooKeeper and etcd, and explore messaging via gRPC and REST. Event-driven design and eventual consistency are emphasized along with patterns like Circuit Breaker, Bulkhead, Retry, and Idempotency. Students examine fault tolerance using replication, sharding, and quorum strategies. Debugging and monitoring tools for distributed environments are introduced. By the end, learners will have the expertise to design concurrent, distributed systems that are resilient, consistent, and performant, making them well-equipped for backend engineering, cloud infrastructure, or systems architecture roles.