Python-Based Interactive Notebooks for Teaching AI and Data Science to Undergraduate Students in the Life Sciences

This chapter presents a reproducible, cloud-based framework for integrating Python-driven data analysis into a large-enrollment undergraduate biochemistry course. Rapid growth in technologies has transformed biochemistry into a data-intensive discipline, yet most life-science students receive little formal training in computational skills. To address this gap, we developed a set of interactive Jupyter notebooks that embed introductory coding, protein structure visualization, metabolic modeling, and simple multi-omics analysis directly within the core biochemistry curriculum. The workflow is delivered entirely through the CyVerse Discovery Environment, which provides browser-based JupyterLab sessions with preconfigured Python environments, version-controlled notebooks, and standardized datasets. This infrastructure eliminates the need for local installation, supports hundreds of simultaneous users, and ensures consistent execution across diverse student hardware. The notebooks follow a scaffolded learn-apply-reflect design that links biochemical concepts with incremental coding tasks, enabling students with no prior experience to manipulate real datasets, generate visualizations, and interpret quantitative patterns. Assessment using validated pre/post surveys across more than 1000 matched responses demonstrated substantial gains in students' confidence with coding and their ability to interpret omics data. The protocol outlined here offers a scalable, equitable model for incorporating computational literacy into biochemistry education and can be readily adapted for related life-science courses.