DSC-2026-01 | No Fear of Numbers – Introduction to Quantitative Data Analysis in R
When?
11 March 2026
09:00 AM - 04:30 PM
Where?
On Campus
MZH | Room 5600
Trainers
Dr. Susanne de Vogel &
Dr. Maryam Movahedifar
Data Science Center, University of Bremen
Number of Participants: Max. 20
Language: English
Why is the topic important?
For many researchers in the social sciences, health sciences, and humanities, data analysis can feel like a barrier – especially without formal training in statistics or programming. Yet, understanding your data and being able to extract meaningful insights from it is a core skill for conducting and interpreting empirical research.
This workshop provides a gentle, practical introduction to data analysis using R – a free, open-source, and widely adopted tool in the research community. Participants learn how to explore and describe data systematically, ask meaningful research questions, and apply basic statistical tools to answer them. With a focus on clear interpretation rather than mathematical detail, this workshop builds foundational skills in working with quantitative data.
By using the tidyverse – a collection of R packages designed to make data handling and analysis more intuitive – participants can perform common statistical tasks using clear, readable code. For modeling tasks such as simple linear or logistic regression, the tidymodels framework builds on the same principles and syntax style, enabling a consistent and accessible workflow from data cleaning to analysis. This lowers the barrier to entry and helps researchers gain confidence in analyzing and communicating their data.
Workshop Goal
By the end of the workshop, participants will be able to describe, compare, and model basic quantitative relationships in their data using R. They will understand key concepts such as distributions, group comparisons, and simple models, and learn how to apply them to real research questions. Participants will also be introduced to core principles of statistical reasoning and result interpretation.
Workshop Content
This workshop is an introduction to descriptive and inferential statistics. It combines basic theoretical input with plenty of hands-on exercises in R using the tidyverse and tidymodels packages, so that you not only understand key concepts but also gain confidence in applying them in practice. You will learn:
- Comprehending the theoretical concepts of descriptive and inferential statistics
- Import, explore, and prepare data for analysis in R
- Describing and visualizing data (e.g. frequencies, distributions, summary stats, ggplot2 graphics)
- Understanding statistical key terms (e.g. p-values, confidence intervals, effect sizes)
- Comparing groups (e.g. crosstabs, t-tests, ANOVA)
- Running and interpreting simple multivariate linear and logistic regression models
- Understand the basic assumptions behind common statistical tests
- Documenting your data analysis
Target Audience & Prior Knowledge
This workshop is a beginners training. It is aimed at researchers in the social sciences, health sciences, and humanities who are working with – or planning to work with – survey-based or other quantitative data, but have little or no prior experience in analysing such data or using statistical software.
No background in statistics is assumed. A little programming experience in R or another language is an advantage, but not a requirement. You don't need to fear numbers – or even better, be ready to leave them behind. All that's needed is a willingness to engage with R and take the first steps into scripting and coding.
Technical Requirements
- Own laptop and stable Wifi connection
(e.g. via eduroam -> /en/zfn/wifi/overview-wifi). - Installation of R Version 4.5.2 and higher (https://cran.r-project.org/) and RStudio Version 2026.01.0+392 and higher (https://posit.co/download/rstudio-desktop/) prior to the course. Both programs are free and open source.
Dr. Susanne de Vogel is a data scientist for training and consulting at the DSC. She holds a diploma in Social Sciences from the University of Cologne (2013) and a PhD in Sociology from the Martin Luther University of Halle-Wittenberg (2019). Susanne has worked for over 10 years on the development and implementation of various panel studies at the German Center for Higher Education Research and Science Studies (DZHW) in Hanover. Her competencies lie in survey design, instrument development, and in the collection, preparation, analysis, and management of (survey) data.
Dr. Maryam Movahedifar holds a PhD in Statistics and has extensive experience in Interpretable Machine Learning. With a strong foundation in statistical methods and practical experience in applying these techniques to real-world problems, she is well-equipped to teach complex machine learning concepts. Her expertise includes making advanced models understandable and accessible.