We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, post-secondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has potential to improve research and learning outcomes and better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated co-curricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants, postdocs, instructors and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

中文翻译：

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生命科学中数据科学教育的集成，模块化方法

我们生活在一个数据驱动的世界，高通量测序和质谱分析平台正在将生物学转变为信息科学。这已将生物学研究的主要挑战从数据生成和处理转移到解释和知识翻译。但是，生物信息学或更普遍的生命科学家数据科学的专科培训落后于当前的需求。特别是，开发可访问的本科数据科学课程，有可能改善研究和学习成果，并为生命科学领域的学生提供更好的准备，使其能够在公共和私营部门的职业中蓬勃发展。在这里，我们描述了本科跨学科教育（EDUCE）计划的经验数据科学，它旨在通过几年的整合实践逐步建立数据科学能力。通过EDUCE，学生可以将数据科学模块完整地集成到必修和选修课程中，并辅以协调的课外活动。EDUCE计划利用了一个实践社区，其中包括来自多个学科的助教，博士后，教师和研究人员，以克服生命科学家对数据科学的报告障碍，包括教师的能力，学生的先验知识以及与学科特定问题的相关性。初步调查结果表明，即使是单个模块，也可以提高学生自我报告的生物信息学和计算机科学的兴趣和/或经验。从而，