Reproducible Quantitative Methods: Data analysis workflow using R
This course is divided into two parts:
4-5 March (part 1) & 18-19 March (part 2).
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 early career researchers (PhD students and post-doctoral researchers) conducting quantitative analyses (ranging from lab-based research to epidemiology).
Learning Objectives
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 early career researchers (PhD students and post-doctoral researchers) conducting quantitative analyses (ranging from lab-based research to epidemiology). By the end of the course, students will have:
1) an understanding of why an open and reproducible data workflow is important,
2) practical experience in setting up and carrying out an open and reproducible data analysis workflow, and
3) know how to continue learning methods and applications in this field.
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.
The course 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 lectures will be given, mainly at the start and end of the course.
TIME & PLACE
Dates: 4-5 March 2019 (part 1) & 18-19 March 2019 (part 2). Note: Attendance all four days is mandatory.
Venue: Milling Hotel Park, Viaduktvej 28, 5500 Middelfart, Denmark
PROGRAMME
INSTRUCTORS
Lead instructor and organizer: Luke Johnston
Instructor: Daniel Witte
Helper: Omar Silverman
Helper: Anna Schritz(for 2 days)
Guest/keynote lecturer
Who can attend?
PhD students or postdoctoral researchers. No experience in data analysis or programming assumed or required. However, before attending the workshop, there are a few prerequisites to complete, which we will provide a few weeks before the course begins.
REGISTRATION & contact
Lead instructor and organizer: Luke Johnston, lwjohnst@ph.au.dk.
To participate in the course, please give a very brief example/description of the type of data you work with and analyze for your research. Please send your answer in a separate e-mail to: lwjohnst@ph.au.dk.
Refer to this website for pre-workshop instructions and to access the teaching material (which might not be online yet).
Deadline for signing up: 25 February 2019.
Seats are allocated on a first come, first served basis.