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Learning Series
Supervised Machine Learning Readiness is a self-paced, beginner-friendly program designed for Earth systems scientists to explore the core principles of supervised machine learning. This series uses a combination of step-by-step frameworks, exploratory widgets, and low-code exercises in Jupyter Notebooks, to explore the full cycle of machine learning model development. No programming experience is required. By the end of the series, you will be able to recognize when machine learning is an appropriate tool and critically evaluate machine learning in Earth systems science contexts.
This work was supported by NSF Unidata under award #2319979 from the US National Science Foundation.
eLearning Module
Replaced with Supervised Machine Learning Readiness
In this module, you will discover how machine learning is used by scientists, the process for supervised machine learning model development, how data plays a crucial role in making good predictions, and how to be an effective and ethical user of machine learning tools. No coding experience is required.
Microlearning
In this module, learners will be introduced to the THREDDS Data Server and Siphon data access architecture and practice making a request for remote data in Python.
This module is recommended for new users of Siphon and the THREDDS Data Server.
Microlearning
In this module, learners will review the multidimensional nature of Earth Systems Science data, explore the hierarchical nature of multidimensional data structures, and search for critical metadata within these structures.
This module is recommended for new users of netCDF data.
eLearning Course
The Advanced Weather Interactive Processing System (AWIPS) is a meteorological software package used for decoding, displaying, and analyzing data. In this course, you will learn the fundamentals of working with the desktop client, AWIPS CAVE, through demonstrations, tutorials, activities, assessments, and challenges. Sections should be completed in sequence but may be completed over multiple sessions.
