Data Analysis (with R) – new modular online format
Live webinars, eLearning, study sessions and individual mentoring with Scavetta Academy
30.10.2023 – 20.11.2023
This workshop will be co-hosted by the IRTG and IMPRS-LS
Sign up with Elizabeth firstname.lastname@example.org
The online workshop will consist of three components:
- An online, independent eLearning start session, including videos eBook and exercises.
- Two 2-hour live Group Sessions
- A live 2-hour Study Session
- An individual 30 minute Mentoring Session for each student
- 30 October 2023 Start eLearning
- 06 November 2023, 9:30 - 11:30 1st live Group Session
- 13 November 2023, 9:30 - 11:30 2nd live Group Session
- 15 November 2023, 14.30 - 16:30 live Study Session
- 20 November 2023, 9:00-17:30 30 min individual Mentoring Session (to be scheduled at beginning of course)
Although the presence time for this course is approx. 5-6 h, committment is necessary to complete independent work within the time frame of available resources! The time investment for this online course is slightly more than a 2 full-day course, but you can organize most of this at your own convenience. Keep in mind: In order to get the most out of the course, reserve time during the span of the course for eLearning and for independent work. A workplan will be provided with the introductory email.
R is an open-source cross-platform software tool that combines data manipulation, statistical modelling and visualisation.
The Data Analysis workshop enables laboratory-based life scientists to use the R statistical programming environment to analyse their own data. This workshop focuses on data manipulation and biostatistics modelling using relevant examples from the life sciences.
Using plenty of hands-on exercises, participants will learn about:
- The most common data structures and functions in R,
- How to manage and ask specific questions of their data, and
- How to use the results of statistical tests.
Packages (e.g. the tidyverse) and paradigms (vectorization) that make R well-suited to data manipulation, as well as common beginner pit-falls, will be introduced.
Basic visualisations will be covered, but will be treated in more depth in the separate Data Visualization workshop.
Methods for dealing with missing data will be broached at various parts in the workshop.
Approximately one third of class time is dedicated to having the students work on their own data-sets under the supervision of the instructor. The goal is to develop data analysis solutions as part of the workshop.
Extra material is provided in the reference book for specific problems, e.g. pattern matching with regular expressions, and control structures (e.g. loops and conditional statements). Participants will be provided with all data-sets and access to the book after the workshop to continue working on these case studies.