Reproducibility and open scientific practices are increasingly demanded of scientists and researchers. Training on how to apply these practices in data analysis is still limited and has not kept up with demand. This course is aimed at researchers conducting quantitative analyses (ranging from lab-based research to epidemiology). By the end of the course, students will have:
Students will develop proficiency in using the R statistical computing language, as well as improving their data and code literacy. Throughout this course we will focus on a general quantitative analytical workflow, using the R statistical software and other modern tools. The course will place particular emphasis on research in diabetes and metabolism; it will be taught by instructors working in this field and it will use relevant examples where possible. This course will not teach statistical techniques, as these topics are already covered in university curriculums.
No experience in data analysis or programming assumed or required. However, before attending the workshop, there are a few prerequisites to complete.
The workshop is structured as a series of participatory live-coding sessions (instructor and learner coding together) interspersed with hands-on exercises, using either a practice dataset or the participants’ own datasets. Some short lectures will be given throughout.
Date and time | Session topic |
---|---|
Tuesday, May 21 | |
9:00-9:30 | Introduction to the workshop, to reproducibility, and to open science |
9:30-12:30 | Project management and best practices (with coffee break) |
12:30-13:30 | Lunch (not provided) |
13:30-17:00 | Data management, wrangling, and best practices (with coffee break) |
17:00-17:15 | End of day remarks and short survey |
Wednesday, May 22 | |
9:00-9:30 | Review of last day’s topics |
9:30-12:30 | Version control and collaborative practices (with coffee break) |
12:30-13:30 | Lunch (not provided) |
13:30-16:30 | Data visualization and best practices (with coffee break) |
16:30-16:45 | End of day remarks and short survey |
Thursday, May 23 | |
9:00-9:30 | Review of last day’s topics |
9:30-12:30 | Creating reproducible documents (with coffee break) |
12:30-13:30 | Lunch (not provided) |
13:30-16:30 | Efficiency in data analysis and best practices (with coffee break) |
16:30-16:45 | Concluding remarks and short survey |