Earth System Data Science in the Cloud

Data Science

Module Goals & Objectives

Specifically by the end of the course, you will have accomplished the following:

• Cleaned data using parallel processing in a distributed cloud environment.
• Prepared data using feature engineering for statistical analysis and machine learning.
  
• Scale analysis from small dev environments to larger testing environments.
• Built reproducible data pipelines to automatically create analysis-ready datasets.
• Applied statistical models in parallel over large earth-system datasets.
• Applied machine learning models at scale to large earth-system datasets.

Module Outline

Days:

1. AI/ML Intro
2. Feature Engineering, Metrics & ML
3. Clusters
4. ML Algorithms & Approaches
5. Team Presentations and Next Steps

Team Project Outline

Days:

1. Data Processing
2. Feature Engineering
3. ML Applications
   • Analysis Ready Data
   • First Run ML
4. Presentation Practice
5. Presentations

Team Project Deliverables

Presentation

• 10 minutes (no more, can be less)
• Team Name
• Research Question (START)
• Data Processing Methods
  • What and Why
• ML Approach
• Next Steps

Strategies for Success

This is a lot of information.

• You would not be here if you could not handle it.
• Be present.
• You will not understand everything the first time. That is OK!
• Keep a Journal of topics to return to and explore more
• You will see each topic/idea at least 3 times on separate days
• Ask questions
• Invest the time now…