Patricia A. Loto
Since 2021, Patricia Loto collaborate on various projects as a member the Metadocencia accessibility team, including the Science Core Bilingual Development project and the Mapping of Communities, Organizations, and Open Science Resources in Latin America. She holds a Bachelor's degree in Information Systems and a Diploma in Data Science, Machine Learning, & its Applications from the FAMAF of the National University of Cordoba. She has taught computational tools and data analysis at varying levels — i.e., for researchers, students, and even people with no formal programming background – at numerous workshops, conferences, and at the Department of Statistical Calculus and Biometry at the Faculty of Agrarian Sciences of the National University of the Northeast. She is also certified to teach programming by The Carpentries and a Tidyverse Instructor by Rstudio. She enjoys learning in community and is an active member of communities such as R-Ladies, the Carpentries, Latin-R, OLS, and The Turing Way, where she contributes and learns from others.
• Machine Learning with Tidymodels in LatinR: https://www.youtube.com/watch?v=1ATHGwDPXQs
• First Steps in R: https://www.youtube.com/watch?v=plE4owAKYNA
• Linkedin: https://www.linkedin.com/in/patricia-loto/
• Website: https://patricia-loto.netlify.app/
• Github: https://github.com/PatriLoto
Sessions
This tutorial walks participants — Earth scientists with some prior Python experience — through analyses of two particular climate risk scenarios: floods & wildfires. The goal is to obtain hands-on experience with common reproducible Jupyter/Python workflows based on data products from the NASA Earthdata Cloud. The case studies highlight the interplay of distributed data with scalable numerical strategies — "data-proximate computing" — implemented using scientific Python libraries like NumPy, Pandas, & Xarray. This tutorial — co-developed by 2i2c and MetaDocencia — constitutes part of NASA's Transform to Open Science (TOPS) initiative to reinforce principles of Open Science & reproducibility.