Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI

Text Visualization (Sommersemester 2019)

Dozent: Prof. Dr. Ralf Krestel (Information Systems) , Tim Repke (Information Systems) , Nitisha Jain (Information Systems) , Julian Risch (Information Systems)
Website zum Kurs: https://hpi.de/naumann/teaching/teaching/ss-19/text-visualization.html

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 26.04.2019
  • Lehrform: Projektseminar
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch
  • Maximale Teilnehmerzahl: 12

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
Data Engineering MA


With the ever increasing volume of data in the modern world, data visualization has become an essential component of every data analysis task. Visualization is an effective way to convey complex information and acts as a bridge between data and decisions.

This seminar will discuss the techniques and tools for creating efficient visualizations for the most important tasks related to large textual datasets.
The seminar is geared to be a series of highly interactive sessions with the students, seeking active classroom participations. The sessions will comprise of topic introductions, brainstorming for ideas, short group activities and active discussions. To maximize the practical learning, students will be expected to submit short practical assignments for the individual topics every week.
The second part of the seminar will consist of a project to be chosen by the student teams.


There are no prerequisites except for being open and motivated to learn about text analysis and tools to analyize and visualize large text collections.


Collection of data visualization tools here.

Lern- und Lehrformen

Students will learn to...

  • perform common text analytics tasks to explore and understand a given text dataset.
  • represent large text corpora in visual form.
  • use various tools to process and visualize text data.
  • understand research papers and report on their own projects' progress.


Students are supposed to do homework assignments in the first phase and to write blog posts throughout the project phase to describe and share their progress.

The final grade will be based on selected homework assignments and the weekly blog posts.


Time: Tuesday, 17:00 (moved from Monday)
Location: Campus II, F-E.06

Date Topic
09.04. Introduction and Organization
16.04. TFIDF/VSM + visualization
23.04. Dimensionality Reduction+PCA+tSNE+ visualization
30.04. Word Embeddings + Visualization
07.05. Topic Modeling + Visualization (ggf. am 9. oder 10.)
14.05. Text Graphs + Visualization
21.05. Knowledge Graph + Visualization
28.05. Advanced topics
04.06. Recap
11.06. Project Pitches
18.06. Projects in groups
25.06. Projects in groups
02.07. Projects in groups
09.07. Projects in groups
16.07. Projects in groups
TBD Final Submission

(subject to change)