Leah has a Ph.D. in Ecology and a Master’s in Landscape Architecture. She has nearly 20 years of experience teaching data intensive science. Previously, she was a senior scientist at NEON (National Ecological Observatory Network) where she developed and lead the Data Skills program. She also has a long standing partnership with the Carpentries and led the development of the Geospatial data lessons in R. Prior to NEON, Leah developed curriculum for and taught geospatial techniques including GIS and high accuracy GPS to land planners, faculty, staff and students in the Penn State Geospatial Technology program. Leah is an outdoor mountain enthusiast and ultra runner. She spends her free time running and fastpacking in the mountains.
Featured Lessons That Leah Has Contributed To
Introduction to Hierarchical Data Formats in Python
You will learn about Hierarchical Data Formats (HDF) and how they can be used to store complex data (and related metadata) such as satellite imagery. You will learn how to open and process HDF files in Python to complete remote sensing analyses.
Introduction to Multispectral Remote Sensing Data in Python
You will learn about various options for multispectral remote sensing data and the advantages and disadvantages of these data options.
Introduction to Writing Clean Code and Literate Expressive Programming
You will learn about the characteristics and benefits of writing clean, expressive code. You will also learn best practices for writing clean, expressive code in Python, including the PEP 8 Style Guide standards.
What Is Open Reproducible Science?
You will learn about open reproducible science and become familiar with a suite of open source tools that are often used in open reproducible science (and earth data science) workflows including Shell, git and GitHub, Python, and Jupyter.