77 Languages
Logo

Apprentice Mode
10 Modules / ~100 pages
Wizard Mode
~25 Modules / ~400 pages
🎓
CREATE AN EVENT

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


Ready to Learn, Share, and Compete?

Language Learning Assistant
with Voice Support

Hello! Ready to begin? Let's test your microphone.
Copyright 2025 @ WIZAPE.com
All Rights Reserved
CONTACT-USPRIVACY POLICY