Seminar: Visual Analytics (BSc, MSc)

Topic Focus:

Visual Analytics (VA) combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning, and decision-making based on very large and complex datasets. As such, research into VA focuses on the combination of the strengths of humans and computers to address complex data analysis challenges. Solutions typically include interactive visual interfaces, enabling users to participate actively in the data science and machine learning process (human-in-the-loop).

Course Mechanics:

In groups, students will study a paper of interest that dictates the individual seminar topic. Students will compile a structured list of references, prepare a compelling fact sheet about the topic, and present the topic in class. In parallel, students will receive mentoring from the supervisor, give constructive feedback on other student's work, and participate actively in the presentation and Q&A rounds.

Learning Goals:

At the end of the course, students:

• have gained a deeper and broader understanding and knowledge of VA
• will be able to identify and discuss research problems and identify relevant related work
• will be able to write a fact sheet about a specific topic in VA research, present the topic to a student audience, and discuss ideas on the topic.
• will have learned to accept and give feedback on students’ seminar contributions

Assessment:

• Fact sheet (25%)
• References list (10%)
• Quality of feedback on other students' fact sheets (15%)
• Presentation slides (15%)
• Presentation of the topic (15%)
• Discussion, Q&A (20%)