1. Foundations of Scalable Data Science

• Big Data concepts (Volume, Velocity, Variety)

• Distributed computing basics

• Parallel processing models

• CAP theorem & consistency models

• Data-intensive system design

 2. Cloud Computing for Data Science

• Cloud service models: IaaS, PaaS, SaaS

• Cloud platforms (AWS, Azure, GCP)

• Virtualization & containerization (Docker, Kubernetes)

• Serverless computing (FaaS)

• Cloud-native architectures

3. Distributed Systems & Architectures

• Distributed system design patterns

• Microservices architecture

• Event-driven systems

• Fault tolerance & consensus algorithms

• Load balancing & scalability strategies

4. Distributed Storage & Data Management

• Distributed file systems (HDFS, GFS)

• NoSQL databases (Cassandra, DynamoDB)

• Data lakes & data warehouses

• Data partitioning & replication

• Data consistency and fault tolerance

5. Big Data Processing Frameworks

• Hadoop ecosystem

• Apache Spark (RDD, DataFrames)

• Stream processing (Kafka, Spark Streaming)

• Batch vs real-time processing

6. Scalable Machine Learning & AI

• Distributed ML training (parameter server, parallelism)

• Deep learning at scale (PyTorch Distributed)

• Federated learning

• Graph ML & large-scale models

• LLM training & inference at scale

7. Data Engineering Pipelines

• ETL/ELT pipelines

• Data preprocessing at scale

• Workflow orchestration (Airflow)

• Data integration & transformation