This comprehensive course introduces cloud computing concepts and services while integrating essential data science skills.
Students will learn to leverage cloud platforms to build, deploy, and scale data-driven applications.
Introduction to Cloud Computing:
Overview of cloud deployment models, service models, and key providers.
Cloud Infrastructure and Services:
Deep dive into compute, storage, networking, and security in the cloud.
Cloud Security and Compliance:
Principles of cloud security, encryption, IAM, and compliance standards.
Cloud DevOps and Automation:
Introduction to CI/CD, IaC, and containerization for cloud deployments.
Advanced Cloud Services and Architectures:
Exploration of serverless computing, big data analytics, IoT, and microservices in the cloud.
AWS, Azure, Google Cloud
Python, Pandas, NumPy, scikit-learn, TensorFlow
Amazon RDS, Azure SQL Database, Google Cloud SQL
AWS EMR, Azure HDInsight, Google Datapost
Matplotlib, Seaborn, D3.js, Plotly, AWS Quick Sight, Power BI, Google Data Studio
AWS Lambda, Azure Functions, Google Cloud Functions
Git, GitHub, Docker, Kubernetes
Security and Encryption
Cloud-based File Encryption Service: Develop a web app for securely uploading and encrypting files using cloud storage.
Cost Optimization
Cloud Cost Optimization Tool: Create a tool to analyze cloud usage and optimize costs by identifying underutilized resources.
Communication and Collaboration
Cloud-based Chat Application: Build a real-time chat app using serverless architecture on AWS or Azure.
Data Analytics and IoT
Cloud-based IoT Data Analytics Platform: Develop a platform for collecting and analyzing data from IoT devices deployed in the field.