Recent technological advancements in computer science, data analytics, and data management have resulted in the acquisition and storage of massive amounts of data across various disciplines. These collections do not reach their full potential unless they are analyzed thoroughly using information extraction, data mining, and knowledge discovery techniques. In this workshop series, we will introduce a comprehensive set of essential methods and approaches in data analytics and scientific computing. This three-week series will include:
- Python and Jupyter notebooks
- Shell scripts, data analysis and discovery, and machine learning
- Version control using GitHub
Computing environments used during the workshop series include Google Collab and the Windows Subsystem for Linux (WSL).
After completion of the workshop series, participants will be able to integrate various scientific computing methods within their workflows to explore image data, extract meaningful patterns from numerical datasets, and perform preliminary analyses. These workflows can be generalized to multiple domains and across biological scales, from individual organismal parts to the whole organisms themselves. In addition, participants will learn the essentials of computing: data pre-processing, statistical analysis, machine learning, data visualization, collaboration, code sharing, and computing environments.
Ariyan Zarei is a Ph.D. candidate in Computer Science at the University of Arizona and is part of the PhytoOracle project.
Emmanuel Gonzalez is a doctoral student in Dr. Duke Pauli’s lab at the University of Arizona. He earned a bachelor’s degree in biology from Pacific Lutheran University.
Travis Simmons is a senior undergraduate student at the College of Coastal Georgia. He joined the University of Arizona’s Pauli Lab as a virtual intern and is now a Research Data Support Specialist.
Nathan Hendler earned a bachelor’s degree at the University of Arizona in geology. His graduate career led to data science, applying statistics and machine learning to large datasets.