Cloud Infrastructure for Data Science
( 25 Modules )

Module #1
Introduction to Cloud Computing
Overview of cloud computing, benefits, and key concepts

Module #2
Cloud Providers Overview
Comparison of major cloud providers (AWS, Azure, GCP, IBM Cloud)

Module #3
Cloud Security Fundamentals
Security concerns, best practices, and compliance in the cloud

Module #4
Data Science in the Cloud
Advantages of cloud-based data science, common use cases

Module #5
Cloud Storage Options
Overview of cloud storage services (S3, Blob Storage, Cloud Storage)

Module #6
Data Ingestion and Processing
Ingesting and processing large datasets in the cloud (Kinesis, Event Hubs, Cloud Pub/Sub)

Module #7
Cloud-based Data Warehousing
Cloud-based data warehousing solutions (Redshift, BigQuery, Synapse)

Module #8
Cloud-based Machine Learning
Overview of cloud-based machine learning services (SageMaker, Azure Machine Learning, AutoML)

Module #9
Containerization for Data Science
Using containers (Docker) for reproducible data science workflows

Module #10
Cloud-based Container Orchestration
Orchestrating containers in the cloud (Kubernetes, ECS, ACI)

Module #11
Serverless Computing for Data Science
Serverless computing concepts and applications in data science

Module #12
Cloud-based Data Visualization
Cloud-based data visualization tools and services (Tableau, Power BI, D3.js)

Module #13
Big Data Analytics in the Cloud
Processing and analyzing big data in the cloud (Hadoop, Spark, HBase)

Module #14
Cloud-based Natural Language Processing
Cloud-based NLP services and applications (NLTK, spaCy, Stanford CoreNLP)

Module #15
Cloud-based Computer Vision
Cloud-based computer vision services and applications (OpenCV, TensorFlow, PyTorch)

Module #16
Cloud Cost Optimization Strategies
Techniques for optimizing cloud costs for data science workloads

Module #17
Cloud Architecture for Data Science
Designing scalable and efficient cloud architectures for data science workloads

Module #18
Cloud-based Collaboration and Version Control
Collaboration and version control tools for data science teams in the cloud (GitHub, GitLab, Bitbucket)

Module #19
Cloud-based Monitoring and Logging
Monitoring and logging tools for cloud-based data science workloads

Module #20
Cloud-based Backup and Recovery
Backup and recovery strategies for cloud-based data science workloads

Module #21
Cloud Security for Data Science
Security best practices for data science workloads in the cloud

Module #22
Cloud Compliance and Governance
Compliance and governance considerations for cloud-based data science workloads

Module #23
Migrating Data Science Workloads to the Cloud
Strategies for migrating on-premises data science workloads to the cloud

Module #24
Building a Cloud-based Data Science Team
Organizational considerations for building a cloud-based data science team

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

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Cloud Infrastructure for Data Science ( 25 Modules )

Cloud Infrastructure for Data Science
( 25 Modules )