• Introduction


    In this course we study approaches for explorative information visualization. The idea is to support information usability by enabling to explore interesting patterns from datasets in visual ways. Explorative information visualization makes a joint use of efficient metaphors like hierarchies, graphs, charts, lists, maps, and timelines.


    The course supports students to understand the role of hybrid methods from spatial data mining to network analytics, and from linked data to time-series handling for supporting information visualization. Our focus is on the process-thinking, thus starting from sparse datasets and to understand the tasks for iteratively making sense of data. In the course we will have a special emphasis on visualizing spatial and temporal information jointly with thematic information. 

    The course builds on the idea of flipped classroom and blended learning ideas, and thus combines online learning materials with intensive face to face sessions. Online materials consist of lectures for preparing, handling and analyzing data, integrating different datasets, and for approaches to create visual demonstrations of data with maps, timelines and thematic overviews. 

    The course consists of sessions each having a brief lecture, discussions, group works and presentations.  The theoretical part of the course is deepened via our joint sessions and by individual seminar reports on selected topics. 


    Learning Outcomes in three categories (must know, should know, nice to know):

    Must know: Basics about how to visualize information in interactive and explorative ways.  
    For this the students will learn how to support information usability for creating visualizations with web technologies. Course is motivated by showing real examples of dealing with spatial, temporal and thematic information, and solutions to them. Those are required to be well understood as a result of the course.

    Should Know: Handling spatial, temporal and thematic data in creative ways. Understanding how to query only the part of data that is useful in a given aggregation, visualization or browsing function is an example of this. Included are different explorative visualization strategies, and understanding of space and time as major integrators for data. As part of this, students should know the requirements for data, and data descriptions for various visualization and application scenarios.

    Nice to know: The works of other students in more detail, i.e. topics, research problems, provided methods and solutions presented in them are material in the course, and belong to this category. Students are introduced to other topics via discussion sessions, presentations, and via the peer review process where students are required to give feedback to other works.

    In summary students will learn theory, techniques, presentation and organizational skills for creating explorative information visualizations.


    Assessment Methods and Criteria: 

    Grading of the seminar is as follows: 1/3 for the seminar work, 1/3 for the opponent work, 1/3 for the active participation and presentation of the seminar work. As part of the active participation grade, students are required to prepare a few one page abstracts of the assigned readings / lectures in relation to her own topic.

    Study Materials:

    Online videos, tutorials and exercises, face to face sessions, articles 

    Grading Scale: 0-5 

    Language of Instruction:  English (default) or Finnish depending on implementation