R Basics – Statistical Data Analysis
21. February 2020 09:00 – 22. February 2020 17:00
Philipps-Universität Marburg, MARA, F|05, Deutschhausstraße 11+13, 1st floor, seminar room 01.0010
R has become one of the most popular statistical programming language and data visualization environment in academia and beyond.
In this two day training, you will learn the basic usage of R enabling you to solve your own data analytics challenges.
Intended Learning Outcomes
- You will understand the statistical programming workflow and basic syntax of R.
- You will know how to read and process data sets.
- You will be able to create visualizations from command line.
- You will understand how to apply common statistical tests and models, such as t-test, regression and ANOVA, and interpret the results.
- You will gain an overview on how to automatize your workflows and make your research reproducible.
Trainer input, hands-on tutorials, practical exercises
You are requested to bring your own laptop with R installed for the use in the training. We suggest to install Anaconda, a data science platform which includes RStudio and Jupyter as an interactive notebook. It can be downloaded at no costs from www.anaconda.com/distribution/. Further instructions will be provided ahead of the training.
Please note that this is not an English language course. Proficiency in English at the B2 level of competency is required.
Please pay attention to our preparatory course "Statistik-Crashkurs" (January 23 and 24, 2020, 9:00–17:00 h), in which you have the possibility to refresh your statistical knowledge.
February 21, 2020, 9 am–5 pm, and
February 22, 2020, 9 am–5 pm
Doctoral candidates from all disciplines
At firstname.lastname@example.org, deadline February 6, 2020
We kindly ask you to confirm your registration to make it binding. You may cancel your registration up to twelve days before the beginning of the course without providing specific reasons. Thereafter, you will be required to pay the attendance fee even if you do not attend the course.
Dr. Matthias Duschl and Dr. Daniel Lee
MArburg University Research Academy (MARA)
Doctoral Program for Life and Natural Sciences