<p>Computational skills are often an afterthought when it comes to broadening students’ expertise in biology—and the quantitative skills necessary for today’s domains of research are only taught in a few classes that many may not end up taking. Students have varying backgrounds in coding vs. domain knowledge:
<p>Computational skills are often an afterthought when it comes to broadening students’ expertise in biology—and the quantitative skills necessary for today’s domains of research are only taught in a few classes that many may not end up taking.
<ul>
<li>Skills being taught aren’t real-world: learning about things that are in isolation, but no chance to apply to actual problems</li>
<li>Students have varying backgrounds in coding vs. domain knowledge: goal is to standardize the experience for both new and returning students</li>
<li>Skills being taught aren’t real-world: learning about things that are in isolation, with few chances to apply to actual problems</li>
<li>Students have varying backgrounds in coding vs. domain knowledge on teams, and there is a need to standardize the experience for both new and returning students</li>
</ul>
Given the wide-ranging suite of tools that each of our computational teams use, our computational biology training workflow was customized for each team. Each team’s project leads created a custom Jupyter notebook aggregating important foundational literature, relevant tools being used at the current stages of their project, and a mini-experiment allowing students to briefly demonstrate their mastery of concepts by applying them to generate novel data or code for their project. Through the training workflow, all members gained a strong conceptual foundation for their project through collaborative onboarding of these tools and exposure to real-world datasets and softwares that could be utilized in their project downstream. These notebooks are now accessible to the public on our GitHub: https://github.com/igematberkeley/compbio-training.
