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.

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.

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)