Information Visualization

6 EC

Semester 2, period 4

5204INVI6Y

Owner Master Artificial Intelligence
Coordinator dr. ing. Jan Zahalka
Part of Master Artificial Intelligence, Master Information Studies, track Human Centered Multimedia, year 1Master Information Studies, track Data Science, year 1Master Computer Science (joint degree), track Big Data Engineering, Master Information Studies, track Information Systems, year 1Master Forensic Science, year 2

Course manual 2020/2021

Course content

Getting insight in large collections of data requires an intricate interplay between data analysis, data mining, domain knowledge, visualization, and interacting users. In this course we will study the development of methodologies which support the process of gaining insight in large and complex datasets by a combination of data analysis, machine learning, and information visualization. Methods are geared towards designing and realizing information visualizations which in an optimal way support the insight gaining process.

Study materials

Literature

  • Scientific papers made available via blackboard.

Objectives

  • Categorize different information visualization techniques, understand their characteristics and knowing when to apply them.
  • Understand the algorithms underlying information visualization techniques.
  • Develop Information Visualizations.
  • Understand visual analytics models.
  • Gather, select, structure, and visualize data in a structured way.
  • Use knowledge of perception and cognition to make effective visualizations.
  • Use design guidelines (e.g. dashboards, aggregation, multiview) for developing effective visualizations.
  • Design interaction models.
  • Design of information visualization evaluation schemes.

Teaching methods

  • Lecture
  • Lectures on the theory of information visualization and visual analytics.
  • Programming sessions in JavaScript, Python, and D3.
  • Computer lab session/practical training
  • Presentation/symposium
  • A large information visualization project in which you design, develop, and present a complete information visualization solution.
  • Lectures on the theory of information visualization and visual analytics.
  • Programming sessions in JavaScript, Python, and D3.
  • A large information visualization project in which you design, develop, and present a complete information visualization solution.

Learning activities

Activity

Number of hours

Hoorcollege

24

Laptopcollege

44

Tentamen

3

Zelfstudie

97

Attendance

This programme does not have requirements concerning attendance (OER part B).

Assessment

Item and weight Details

Final grade

1 (50%)

Tentamen

1 (50%)

Group project

Passing grade (>= 5.5) required for both components

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer Onderwerpen Studiestof
1
2
3
4
5
6
7
8

Timetable

The schedule for this course is published on DataNose.

Additional information

 Required skills: Programming skills (in particular JavaScript and/or Python), knowledge of data mining.

Contact information

Coordinator

  • dr. ing. Jan Zahalka