Cloud Computing for Data Analysis
This book is 20% complete
Last updated on 2020-01-24
About the Book
After reading this book you will be able to:
- Summarize the fundamentals of cloud computing
- Evaluate the economics of cloud computing
- Accurately evaluate distributed computing challenges and opportunities and apply this knowledge to real-world projects.
- Develop non-linear life-long learning skills
- Build, share and present compelling portfolios using: Github, YouTube, and Linkedin.
Chapter One: Getting Started
- Effective Async Technical Discussions
- Effective Async Technical Project Management
- Cloud Onboarding
Chapter 2: Cloud Computing Foundations
- Overview of Cloud Computing
- IaC (Infrastructure as Code) w/ Terraform
- What is Continuous Delivery and Continuous Deployment?
- Continuous Delivery for Hugo Static Site from Zero
Chapter3: Virtualization & Containerization
- CPU, Memory, I/O
Chapter 4: Challenges and Opportunities in Distributed Computing
- CAP Theorem
Chapter 5: Cloud Storage
- Cloud Databases: HBase, MongoDB, Cassandra, DynamoDB, Google BigQuery
Chapter 6: Serverless
- Cloud 9 Development Environment
Chapter7: Big Data Platforms
- Batch Processing: EMR/Hadoop, AWS Batch
Chapter 8: Managed Machine Learning Systems and Platforms
- AWS Sagemaker
Chapter9: Edge Computing
- IoT: AWS Greengrass, Raspberry Pi
Chapter 10: Case Studies
- Datascience meets intermittent fasting
Chapter 11: Essays
- Why There Will Be No Data Science Job Titles By 2029
- Exploiting The Unbundling Of Education
- How Vertically Integrated AI Stacks Will Affect IT Organizations
- Here Come The Notebooks
- Cloud Native Machine Learning And AI
Chapter 12: Cloud Certifications
- AWS Certification Guide
- GCP Certification Guide
Chapter 13: Career
- How to build a Portfolio
- How to land a job
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