# Bridging the Educational Gap between Emerging and Established Scientific Computing Disciplines

## TL;DR

This paper discusses developing interdisciplinary graduate courses in emerging computational fields like biology and medicine, combining theory and practice to address educational gaps and improve student skills.

## Contribution

It introduces a curriculum design integrating theoretical and practical training for computational biology and medical science students, highlighting methods and outcomes.

## Key findings

- Students lack sufficient instruction in statistical and analytical skills.
- The curriculum improves student understanding and interdisciplinary collaboration.
- Identified weaknesses in students' foundational knowledge.

## Abstract

In this paper we describe our experience in developing curriculum courses aimed at graduate students in emerging computational fields, including biology and medical science. We focus primarily on computational data analysis and statistical analysis, while at the same time teaching students best practices in coding and software development. Our approach combines a theoretical background and practical applications of concepts. The outcomes and feedback we have obtained so far have revealed several issues: students in these particular areas lack instruction like this although they would tremendously benefit from it; we have detected several weaknesses in the formation of students, in particular in the statistical foundations but also in analytical thinking skills. We present here the tools, techniques and methodology we employ while teaching and developing this type of courses. We also show several outcomes from this initiative, including potential pathways for fruitful multi-disciplinary collaborations.