Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI

Visual Data Exploration and Story Telling - the good, the bad, and the ugly (Sommersemester 2023)

Dozent: Prof. Dr. Bernhard Renard (Data Analytics and Computational Statistics) , Dr. Christoph Schlaffner (Data Analytics and Computational Statistics) , Simon Witzke

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 01.04.2023 - 28.04.2023
  • Lehrform: Seminar
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch
  • Maximale Teilnehmerzahl: 12

Studiengänge, Modulgruppen & Module

Data Engineering MA
IT-Systems Engineering MA
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-K Konzepte und Methoden
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-T Techniken und Werkzeuge
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-S Spezialisierung
Cybersecurity MA
Digital Health MA
Software Systems Engineering MA


Data contains information in various flavours and is readily available across all aspects of life. This brings an increased potential and complexity to understanding and analysing the heaps of data points with it. Data visualisation is a key method to disentangle and grasp the structure and relationships within datasets. Graphical representation does not only enable exploration but is instrumental in communicating results and findings to varied audiences. In this seminar, we will examine methods of visual data exploration and discuss their effective application across different use cases. Furthermore, we will dive into the question of which story the data tell and which story we want to convey. Therefore, amongst other aspects we will investigate types of visualisation techniques and their advantages and disadvantages in delivering the intended message to different target groups.

Topics include:

  • Data Types and their implications
  • Different visualisation methods ranging from simple bar charts to more complex visualisations such as hierarchical heatmaps or network graphs
  • Normalisation and dimensionality reduction techniques
  • Pre-attention via colour, glyphs and more
  • Tailoring your visualisation to your audience


Solid knowledge of at least one scriping language for data visualization such as R or python. 


Unwin, A. (2020). Why Is Data Visualization Important? What Is Important in Data Visualization? Harvard Data Science Review, 2(1). doi.org/10.1162/99608f92.8ae4d525

Rendgen, S. History of information graphics. (Taschen, 2019).

Rendgen, S. et al. Information graphics. (Taschen, 2012).

Beautiful, I. is. Information is Beautiful. Information is Beautiful


McCandless, D. Information is beautiful. (Collins, 2012).

McCandless, D. Knowledge is beautiful. (William Collins, 2014).

Sage. Information Visualization. (SAGE, 2002)

Springer. Journal of Visualization. (Springer, 1998)

Lern- und Lehrformen

We do not aim to teach the use of specific tools, but how to effectively choose and use visualisation as a method to understand and convey information.

The seminar mode will be student lead lectures, each covering a different topic. We will provide literature to guide you through each topic and will also include examples of the good, the bad, and the ugly of data visualisations.

The student lead lectures will provide you with the knowledge to address a data visualisation task towards the second half of the semester. During these tasks you will explore different datasets visually and prepare a presentation for your peers and a small report.

Please register in the moodle of the course for further information.


The final grade will be compiled from the held lectures and the results of the final visual data project. 

The actual grade will be determined based on: 40% Student held lecture, 40% Visualization project and project presentation, and 20% final report.


Last possible resignation: 28.4.2023