一个关于生物医学数据科学短课程的教学建议.
Davide Chicco1,2, Vasco Coelho1
1Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy.
PLoS computational biology
|April 14, 2025
概括
本研究概述了生物医学数据科学士学位课程,重点是数据分析和解释. 它强调了数据处理,机器学习和开放科学原则的实际技能,以培训未来的数据科学家.
科学领域:
- 生物医学数据科学 生物医学数据科学
- 计算统计学 计算统计学
- 机器学习 机器学习
背景情况:
- 生物医学大数据的数量不断增加,需要为大学生提供专门的培训.
- 现有的课程可能不充分涵盖分析和解释健康数据的实际方面.
研究的目的:
- 提出和描述生物医学数据科学士学位课程计划.
- 分享在过去学年实施课程的实践经验.
主要方法:
- 课程开发侧重于数据采集,清洁和准备.
- 关于探索性数据分析 (EDA),机器学习 (监督/无监督) 和结果验证的说明.
- 整合开放科学原则,利用开源工具和数据.
主要成果:
- 学生们接受了培训,以识别,清洁和准备开放的生物医学数据集.
- 统计和机器学习技术的实际应用与结果的解释.
- 强调使用开源编程语言 (R,Python) 和开源资源.
结论:
- 拟议的课程有效地为学生提供了必要的生物医学数据科学技能.
- 该课程在开放科学框架内促进数据分析和解释的最佳实践.
- 这个教学建议是开发新的生物医学数据科学课程的宝贵资源.
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