5936 Data-based Storytelling
Daniel Winkler, MSc (WU), Stephan Fally, MSc (WU), MSc
Contact details
Weekly hours
Language of instruction
02/21/24 to 02/28/24
Registration via LPIS
Notes to the course
Day Date Time Room
Tuesday 03/12/24 01:00 PM - 04:00 PM TC.3.08
Thursday 03/14/24 03:00 PM - 06:00 PM TC.3.08
Thursday 03/21/24 03:00 PM - 06:00 PM D2.0.030
Tuesday 04/09/24 01:00 PM - 04:00 PM Online-Einheit
Thursday 04/11/24 03:00 PM - 06:00 PM D4.0.133
Tuesday 04/16/24 12:30 PM - 02:00 PM Online-Einheit
Tuesday 04/23/24 12:30 PM - 02:30 PM TC.4.12
Tuesday 04/30/24 01:00 PM - 05:00 PM D2.0.342 Teacher Training Raum

Both local start-ups such as "Gurkerl" and multinational companies such as "Amazon" have invested heavily in their data science departments. Experiments and machine learning algorithms are running around the clock to gain insights from huge amounts of data. Nevertheless, the insights gained need to be translated into actionable business decisions. This can lead to ineffective communication when data scientists and business leaders use divergent language.

This course offers students the opportunity to bridge the gap between data science and business decisions and become "data translators". The course offers insights into the interaction of data, narratives and visualizations, and their underlying neuroscientific concepts. Students will learn how to communicate data effectively when the intended audience is not familiar with statistical and data science jargon. Specifically, this will be done via visualizations. Students will be introduced to the learning content both theoretically and practically.

This course is divided in three parts:

  1. Theory and practice of storytelling;
  2. Causal inference;
  3. After a brief introduction to the neuroscience of perception, we will focus on its application to the theory and practice of visualization.

This course uses the R programming language to interpret and present data. No prior knowledge of R is required.

Learning outcomes

Methods, principles and theories of data interpretation and visualization as well as communication of data-based analyses and their application in a business setting.

The objectives of the course are:

  • To become a translator between data scientists and business leaders;
  • To learn how to interpret and communicate causality within a business setting;
  • To learn how to effectively communicate data-based analyses using appropriate, neurally-effective visualization and presentation techniques;
  • To learn how to analyze and interpret data using the R programming language.
Attendance requirements

You must attend at least 80% of all dates in order to pass the course. All missed appointments must be compensated with an essay on the material covered. This applies to both face-to-face and online units (should the latter be required). Attendance is compulsory for both the first unit and the final exam.

Teaching/learning method(s)

The course is taught through a combination of interactive sessions, class discussions and student presentations. Theories will be applied to real-world examples and implemented by students in a mini-project at the end. The goal is to create an open learning environment that encourages trial-and-error, discussion, and further development of practical skills for data-driven businesses. The focus of the course is on (the neural representation of) data visualization and the tools required to create interpretable and engaging diagrams for different audiences.

To be prepared for the lectures, you will need to work through the assigned material for the week and be prepared to answer questions about it. The sessions themselves are designed to (1) identify and apply theoretical concepts and (2) clarify unclear points that need further discussion.


The grading is made up as follows:

  • Group presentations (40%)
  • Final exam (30%)
  • Participation in discussions based on the study material (written or oral; 10%)
  • Homework (20%)

The grading scheme is defined as follows

< 60% not sufficient (5)

60% to 69.99% sufficient (4)

70% to 79.99% satisfactory (3)

80% to 89.99% good (2)

>= 90% very good (1)


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Recommended previous knowledge and skills

Basic knowledge of statistics.

Availability of lecturer(s)

Upon request.

Unit details
Unit Date Contents

Intro to storytelling:

  1. Introduction to the course and to the concept of data translators
  2. Storytelling theory: how do we build a good story?
  3. Effects of stories on our brain and behavior: main protagonists in and neuroscience of stories.


  1. Introduction to the science of visualizations
  2. Visual search: how do we search for information? 
  3. Perception of colors: how do we use colors in visualizations?
  4. Guiding of attention: how do we make visualizations neurally efficient?



Causal Inference


Data presentation and visualizations in R


Putting it all together:

  1. Memorability: how do people remember visualizations? 
  2. Cognition: how do we deal with information?
  3. How to build a causal visual story





Group presentations

Last edited: 2024-01-13