Analyses associated with "An integrated, modular approach to teaching data science in microbiology"
The life sciences posses a need for data science competency as 'big data' and reproducible analyses increasingly become the norm. However, training in bioinformatics, or more generally data science for life scientists, lags behind current demands and predominately focuses on graduate or early career opportunities. Thus, required data science curriculum for undergraduate students may help to overcome the continued widespread need for training. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competencies across two years of undergraduate coursework. In this program, students complete data science modules (2 - 17 hrs) integrated into required courses as well as have opportunities to pursue additional training through calibrated co-curricular activities. The EDUCE program draws on a cross-disciplinary community of practice 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 framework for integration of effective data science curriculum into a diverse range of undergraduate programs.
flowcharts/
- Curriculum and course flow charts
- Figure 1. EDUCE module overview.
- Figure 2. EDUCE curriculum in microbiology.
R_figures/
- EDUCE_survey_cleanup
- Rmarkdown of survey data cleaning to create data_clean/
- data_clean/
- Cleaned survey data from Fall 2017 through Spring 2019
- Contains only students who consented to inclusion in the study
- Raw data available upon IRB or BREB (human ethics approval)
- EDUCE_desc_pub_figs
- Figure 3. EDUCE team members at UBC.
- Figure 4. Student interest and experience in data science.