Using interactive data visualization to make sense of large datasets

Description

As sensor technology improves, data volumes grow. We now live in a sea of data collected by our phones, smartwatches, and home assistants like Alexa. Science is not any different, new sensors are enabling the collection of large datasets that can be mined for new scientific discoveries. In plant science, sensor technology is being applied to study how plants grow under drought conditions. This workshop will introduce you to common data wrangling Python packages: Pandas allows us to interact with data related to plant growth, while Plotly Express allows us to generate interactive visualizations to make sense of these data. You will learn how to open data, filter data, slice data, and generate informative interactive visualizations from large datasets. Knowing how to handle and make sense of data will be increasingly important, and this workshop is your first step towards that! 

Required experience & hardware

This workshop is aimed at anyone who is learning to code or advanced coders with little experience in data visualization. Command line and/or programming experience in Python is helpful, but not required. A computer with internet access is necessary to join the workshop and access the learning materials.

Presenter bio

Emmanuel Gonzalez is a Ph.D. student at the University of Arizona’s School of Plant Sciences. Emmanuel is a hybrid scientist: part plant scientist, part data scientist. His research focuses on developing scientific computing software to process large volumes of image and 3D data collected from agricultural fields. As a plant scientist, Emmanuel studies how plants grow and respond to drought conditions by leveraging his data scientist skills in High Performance Computing, Applied Machine Learning, and Geographic Information Science.

This workshop is part of the University Libraries' Digital Scholarship & Data Science Fellowship program. You can find out more about the program and other workshops being offered through the series at https://data.library.arizona.edu/data-science/ds2f. If you have questions about the program, please e-mail Jeff Oliver (jcoliver@arizona.edu).