Syllabus

  • Part 1: Introduction to Information Visualization and Interaction Design
  • Definitions: DataVis, InfoVis, SciVis, Interaction Design
  • Course roadmap, tools (D3, Vega-Lite, Unity, WebGL)
  • Key concepts: interaction paradigms, storytelling, insight generation
  • Part 2: Perception, Cognition, and Data Structures in Visualization
  • Visual perception, Gestalt laws, attention and memory
  • Visual encoding basics: position, shape, size, color, motion
  • Data types and structures: tabular, hierarchical, relational, spatial, temporal
  • Part 3: Visual Encoding and Design Principles
  • Expressiveness and effectiveness
  • Chart taxonomy: bar, line, area, pie, scatter, network, matrix
  • Choosing visual encodings
  • Task-based design: lookup, comparison, overview, filter, explore
  • Part 4: Multivariate and High-Dimensional Visualization
  • Visualizing multivariate data: glyphs, scatterplot matrices, parallel coordinates
  • Dimensionality reduction: PCA, t-SNE, UMAP
  • Encoding multiple variables effectively
  • Visual abstraction and reduction techniques
  • Part 5: Interaction Techniques in Visualization
  • Interaction models: direct manipulation, brushing, linking, zooming, filtering
  • State management and user feedback
  • Dashboard composition and exploratory interfaces
  • Part 6: Uncertainty Visualization
  • Types of uncertainty: data-level, model-level, perceptual
  • Visual encoding of uncertainty: error bars, blur, animation
  • Cognitive biases and visual trust
  • Applications in AI and simulation
  • Part 7: Geospatial Visualization
  • Coordinate systems, map projections, spatial joins
  • Choropleths, dot maps, heatmaps, symbol maps
  • Spatial-temporal data and dynamic rendering
  • Part 8: Temporal and Spatiotemporal Visualization
  • Time series: line charts, small multiples, horizon graphs
  • Calendars, event sequences, animations
  • Combining spatial and temporal layers
  • Part 9: AR/VR for Data Visualization
  • Principles of immersive visualization
  • Head-mounted display (HMD) environments vs. handheld AR
  • Spatial interaction and multi-modal input
  • Case studies in scientific and urban-scale data
  • Part 10: Machine Learning and Explainable Visualization
  • Model visualization (trees, layers, embeddings)
  • XAI tools: SHAP, LIME, saliency maps
  • Visual analytics for black-box models
  • Ethics, bias, and decision support
  • Part 11: Real-Time Visualization and Visual analytics
  • Progressive rendering, streaming data, sketch-based rendering
  • Performance optimization in web contexts
  • Principles of visual analytics: combining automated analysis with interactive visualization
  • Sensemaking and decision-making based on visualization
  • Part 12: Storytelling with Data
  • Narrative techniques in visualization
  • Annotated charts, scrollytelling
  • Case studies (NYT, Gapminder, Datawrapper)
  • Part 13: Collaborative Visualization and Multi-User Systems
  • Synchronous and asynchronous collaboration
  • Case studies: collaborative dashboards, citizen science, education
  • Part 14: Project Studio and Critique
  • Design critiques: project iteration and peer feedback
  • Evaluation frameworks for InfoVis: insight-based metrics, usability
  • Wrap-up discussion: the future of interaction and visualization
  • Part 15: Final Project Presentations
  • Final project demos and walkthroughs
  • Peer + instructor feedback
  • Submission of report and code/artifacts
  • At most of the lectures, last hour will be dedicated to presentation of state-of-the-art works from the corresponding topic

Readings

  • Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.
  • Munzner, T. (2014). Visualization analysis and design. CRC press.
  • Ware, C. (2019). Information visualization: perception for design. Morgan Kaufmann.
  • Kirk, A. (2019). Data visualisation: A handbook for data driven design. (pp. 1-328).
  • Tominski, C. (2015). Interaction for visualization. Morgan & Claypool Publishers.
  • Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.
  • Rosenberg, D. & Grafton, A. (2013). Cartographies of time: A history of the timeline. Princeton Architectural Press.
  • McCandless, D. (2012). Information is beautiful. London: Collins.
  • Steele, J. & Iliinsky, N. (2010). Beautiful visualization: Looking at data through the eyes of experts. " O’Reilly Media, Inc. .
  • Fry, B. (2007). Visualizing data: Exploring and explaining data with the processing environment. " O’Reilly Media, Inc. .

Objectives and Competences

  • Design and implement interactive data visualizations using appropriate visual encodings, layouts, and user interaction techniques.
  • Analyze and evaluate data visualization systems with respect to usability, cognitive effectiveness, and visual perception.
  • Integrate geospatial, multivariate, temporal, and uncertain data into coherent and effective visual representations.
  • Develop immersive visualizations using AR/VR frameworks for spatial data exploration and interactive storytelling.

Competences:

  • Understand the theoretical foundations of information visualization, including human perception, visual encoding, and interaction models.
  • Learn to select appropriate visualization techniques based on data types, analysis goals, and user needs.
  • Explore the role of interaction in supporting exploratory data analysis and storytelling across various domains.
  • Critically assess the ethical, cognitive, and communicative dimensions of visual representations in real-world applications.

Intended Learning Outcomes

Students will be able to:

  • Demonstrate a critical understanding of the theoretical foundations of information visualization, including perceptual and cognitive principles, visual encoding, and interaction models.
  • Design and implement interactive visualization systems that effectively communicate patterns in complex data, including geospatial, temporal, multivariate, and uncertain datasets.
  • Develop immersive and spatial visualizations using AR/VR technologies and 3D interaction techniques for real-time or context-aware data exploration.
  • Select and justify appropriate visualization techniques and tools for specific data types, analytical goals, and user contexts within real-world applications.
  • Communicate design decisions and technical solutions effectively through visual storytelling, documentation, and presentation of interactive prototypes.

Assignments

Type (examination, oral, coursework, project): 50,00 % Continuing (homework, midterm exams, project work) Final (written and oral exam) Grading system: 5 - 10, a student passes the exam if he is graded from 6 to 10