In today’s data-driven world, mastering cloud computing is essential for every data science professional. This course is designed to give you hands-on experience with the three major cloud platforms—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—specifically tailored for data science and machine learning workflows.

Under the guidance of Dinesh Patturi, a cloud expert with 7 years of industry experience, you'll learn how to process, store, analyze, and deploy data science models using scalable cloud solutions. From foundational services to advanced ML tools, this course blends practical skills with real-world projects to prepare you for high-demand roles in cloud data engineering, ML ops, and full-stack data science.

Whether you're a beginner exploring cloud platforms or a working professional aiming to specialize in cloud-based data science, this course provides the tools and confidence to build production-ready, cloud-native data systems.
Module 1: Introduction & Fundamentals of Cloud Computing

• What is Cloud Computing? Definitions & Key Concepts (IaaS, PaaS, SaaS)

• Deployment Models: Public, Private, Hybrid, Multi‑Cloud

• Overview of Major Cloud Providers: AWS, Azure, GCP

• Global Infrastructure: Regions, Zones, Edge Locations

Module 2: Core Services & Components

• Compute Services: EC2 (AWS), Virtual Machines (Azure), Compute Engine (GCP)

• Storage Services: S3 / Blob Storage / Cloud Storage, Block Storage, File Storage

• Databases: Relational & NoSQL (RDS, Azure SQL, Cloud SQL, DynamoDB, Firestore)

• Networking Basics: VPC / Virtual Networks, Subnets, Security Groups / NSGs, Load Balancers

Module 3: Data Science Tools & Cloud Integration

• Processing Data in the Cloud: S3 / Azure Blob / GCS usage

• Big Data and Analytics Basics: Data warehousing, Data lakes, Querying large datasets

• Tools like AWS Athena, Azure Synapse, BigQuery

• Compute for Data Science: Using GPU / High‑CPU instances; Serverless computing (Lambda, Cloud Functions, Azure Functions)

Module 4: Machine Learning Workflows on the Cloud

• ML Model Training & Deployment: Using managed ML services (SageMaker, Azure ML, AI Platform)

• Data Pipelines & ETL processes in cloud environment

• Model versioning, monitoring, and data drift detection

• Integration with notebooks (Jupyter, SageMaker notebooks, Azure notebooks)

Module 5: DevOps for Data Science & Automation

• Infrastructure as Code: Terraform / Azure Resource Manager / GCP Deployment Manager

• CI/CD pipelines for Data Science Projects

• Containerization: Docker & Kubernetes (EKS, AKS, GKE)

• Version control + Experiment tracking

Module 6: Security, Governance & Cost Optimization

• IAM: Identity & Access Management in AWS/Azure/GCP

• Security best practices: Encryption, Key Management, Access control

• Governance, Compliance, Data Privacy (GDPR, HIPAA, etc.)

• Monitoring & Logging: CloudWatch / Azure Monitor / Google Stackdriver

• Cost management: budgeting, rightsizing, reserved instances, spot instances

Module 7: Real‑World Projects & Use Cases

• Project 1: Building a scalable data pipeline across cloud providers

• Project 2: Deploying a Machine Learning model as an API endpoint

• Project 3: Cloud migration case study / migrating on‑prem data to cloud storage with analytics layer

• Project 4: Dashboard with monitoring, alerts, cost visibility

Module 8: Certification Prep & Career Guidance

• Overview of Certifications: AWS Certified Data Analytics, Azure Data Scientist, Google Professional Data Engineer

• Interview Questions & Best Responses in Cloud Data Science roles

• Resume & Portfolio building: Showcasing cloud & data science projects