Cloud Computing for Data Science
( 25 Modules )

Module #1
Introduction to Cloud Computing
Overview of cloud computing, its history, and evolution

Module #2
Cloud Computing Service Models
Exploring IaaS, PaaS, and SaaS service models

Module #3
Cloud Deployment Models
Understanding public, private, hybrid, and community cloud deployment models

Module #4
Cloud Providers and Market Trends
Overview of major cloud providers (AWS, Azure, GCP, IBM) and market trends

Module #5
Data Science in the Cloud
Introduction to data science in the cloud, benefits, and challenges

Module #6
Cloud Storage for Data Science
Exploring cloud storage options (blob storage, object storage, file storage)

Module #7
Cloud Data Warehousing
Introduction to cloud data warehousing, Amazon Redshift, and Google BigQuery

Module #8
Cloud-based Data Lakes
Building data lakes with cloud storage, AWS Lake Formation, and GCP Cloud Storage

Module #9
Cloud-based NoSQL Databases
Exploring cloud-based NoSQL databases, Amazon DynamoDB, and Google Cloud Firestore

Module #10
Cloud-based Relational Databases
Exploring cloud-based relational databases, Amazon RDS, and Google Cloud SQL

Module #11
Cloud-native Data Processing
Introduction to cloud-native data processing, Apache Spark, and Apache Flink

Module #12
Cloud-based Machine Learning
Introduction to cloud-based machine learning, AWS SageMaker, and Google Cloud AI Platform

Module #13
Cloud-based Deep Learning
Introduction to cloud-based deep learning, TensorFlow, and PyTorch

Module #14
Cloud Security and Compliance
Overview of cloud security and compliance, IAM, and data encryption

Module #15
Cloud Cost Optimization
Strategies for cloud cost optimization, cost estimation, and resource utilization

Module #16
Cloud Migration and Deployment
Migrating data science workloads to the cloud, containerization, and serverless computing

Module #17
Cloud-based Collaboration and Version Control
Using cloud-based collaboration tools, GitHub, and GitLab for data science

Module #18
Cloud-based Data Visualization
Cloud-based data visualization, Tableau, Power BI, and D3.js

Module #19
Cloud-based Workflow Automation
Automating data science workflows, Apache Airflow, and Zapier

Module #20
Cloud-based Model Deployment and Management
Deploying and managing machine learning models in the cloud

Module #21
Case Studies in Cloud-based Data Science
Real-world case studies of cloud-based data science projects

Module #22
Best Practices for Cloud-based Data Science
Best practices for cloud-based data science, architecture, and implementation

Module #23
Cloud-based Data Science for Specific Industries
Cloud-based data science applications for healthcare, finance, and retail

Module #24
Future of Cloud Computing for Data Science
Emerging trends and future directions for cloud computing in data science

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Cloud Computing for Data Science career

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