Lecture: Interactive-Visual Data Analysis (L&E)

As of 2022, but still applies to 2025.

General Description 

This course introduces fundamental concepts and techniques of interactive-visual data analysis (IVDA). The main focus is on the combination of automatic data analysis methods with interactive visual interfaces, as well as on their interplay to facilitate data analysis goals. As such, IVDA is particularly suited to leverage the strengths of both humans and machines in a human-in-the-loop data analysis process. Associated research fields are Information Visualization, Visual Analytics, Interactive Data Science, Interactive Machine Learning, and Human-Centered Artificial Intelligence.

Learning Outcome

Students will learn basic characteristics of data types and data attributes (WHAT), as well as data analysis tasks (WHY). Further, students will learn basic design skills about HOW data can be transformed into visual structures and which types of visualization techniques are meaningful design choices for given data types and analysis at hand. Students will also learn fundamental interaction techniques, as well as concepts for the composition of views in data analysis systems for different data types and their individual complexities.

Further, students will gain a deep understanding about how data analysis can benefit from both having a human and a (machine learning) model in the loop, following the goal to gain knowledge from data.  Along these lines, students will learn about the strengths (and weaknesses) of human and machines, as well as about combining these complementary strengths effectively, as described in Visual Analytics methodology. In detail, students will learn examples for interactive data preprocessing, human-centered unsupervised machine learning, as well as for human-centered semi-supervised and supervised machine learning. Finally, the course introduces approaches that allow training personalized machine learning models and conduct personal and human-centered data analytics and artificial intelligence.

Target Groups

This module is designed for MA students (POC, DS, AI). It would be possible for students in other disciplines to take this course with a programming background. There are no enforced prerequisites, but the willingness to participate actively and to work in groups is recommended.  

Prior Knowledge

The course requires some degree of  programming expertise. Student groups should be comfortable with frontend and backend development. Basic knowledge in data science, machine learning, and data analysis are useful but not mandatory.

Recommended Reading

Visualization Analysis and Design, Tamara Munzner (A K Peters Visualization Series, CRC Press, 2014) is the course textbook. Recommended reading also includes selected papers, as outlined below.

Recommended reading (pre-class)

• VAD Book Chapter 1. What's Vis, and Why Do It?

In-class Agenda

• Welcome to the class!
• Data-Intensive Science: The Fourth Research Paradigm

• Historic Introduction: IVDA in Five Stages

• Exercise: IVDA Quiz
• Course Logistics: How the Course wil work

Further Reading

• [VIS Design Principles]: Semiology of Graphics, Jacques Bertin, Gauthier-Villars 1967, EHESS 1998
• [VIS Design Principles]: The Visual Display of Quantitative Information. Edward R. Tufte. Graphics Press, 1983.

Recommended reading (pre-class)

• VAD Book Chapter 2: What: Data Abstraction

In-class Agenda

• What to analyze? Introduction to the data perspective

• What to analyze? Dataset Types, the between-objects perspective

• What to analyze? Data Attributes: nominal, ordinal, and numerical

• Exercise / Live-Demo

Further Reading

• [Data and Tasks]:The eyes have it: A task by data type taxonomy for information visualizations - B. Shneiderman - 1996

• [VIS Introduction]: Readings in Information Visualization: Using Vision To Think, Chapter 1 Stuart K. Card, Jock Mackinlay, and Ben Shneiderman. Morgan Kaufmann - 1999

• [Data and Tasks]: Information Visualization and Visual Data Mining - Daniel Keim - 2002

• [Time Series Data]: Visualization of Time-Oriented Data - Wolfgang Aigner, Silvia Miksch, Heidrun Schumann, Christian Tominski. Revised and Expanded Second Edition. Springer, 2023

Recommended reading (pre-class)

• VAD Book Chapter 3: Why: Task Abstraction

In-class Agenda

• Why analyze? Analysis Tasks - actions and targets

• Why analyze? Data and Task Abstraction using a four-level analysis framework for design and validation

• Exercise / Live-Demo

Further Reading

• [Paper]: (The Nested Model) A Nested Model for Visualization Design and ValidationTamara Munzner. IEEE TVCG. 2009

• [Paper]: (Data and Task) The eyes have it: a task by data type taxonomy for information visualizations Ben Shneiderman. IEEE Symposium on Visual Languages. 1996

• [Paper]: (Data and Task) Information Visualization and Visual Data MiningDaniel Keim. IEEE TVCG. 2002

Recommended reading (pre-class)

• VAD Book Chapter 5: Marks and Channels

In-class Agenda

• How analyze? Marks - Basic Graphical Elements

• How analyze? Channels - Visual Variables

• How analyze? Visualization Guidelines - Perception, Color, and Rules of Thumb

• How analyze? Decoding of Visualizations - Chart Decomposition

• Exercise / Live-Demo

Further Reading

• [VIS Design Principles]: Semiology of Graphics, Jacques Bertin, Gauthier-Villars 1967, EHESS 1998
• [VIS Design Principles]: The Visual Display of Quantitative Information. Edward R. Tufte. Graphics Press, 1983.

In-class Agenda

• How analyze? Interaction Design - Engaging in a dialog with the data

• How analyze? Interaction Techniques - Overview of atomic Interactions

• How analyze? View Composition - Leveraging interaction techniques

• Exercise / Live-Demo

Further Reading

• Polaris: A System for Query, Analysis and Visualization of Multidimensional Relational Databases.

  Chris Stolte and Pat Hanrahan. Proceedings of IEEE InfoVis 2000. [research paper, intellectual foundation of the Tableau software]

• [ND2002] Norman, D., The design of everyday things: Revised and expanded edition. Basic books. 2013

• [TS2020]: Interactive Visual Data Analysis. Christian Tominski and Heidrun Schumann. AK Peters Visualization Series. CRC Press. 2020

Recommended reading (pre-class)

• VAD Book Chapter 7: Arrange Tables
• VAD Book Chapter 8: Arrange Spatial Data
• VAD Book Chapter 9: Arrange Networks and Trees
• Polaris: A System for Query, Analysis and Visualization of Multidimensional Relational Databases. Chris Stolte and Pat Hanrahan. Proceedings of IEEE InfoVis 2000. [research paper, intellectual foundation of the Tableau software]

In-class Agenda

Advanced visualization techniques for...

• Multivariate Data
• Networks & Graphs
• Trees & Hierarchies
• Time Series
• Geographical Data
• Other Data Types

Further Reading

Web-based overviews of techniques for...

• Multivariate Data
• Networks & Graphs
• Trees & Hierarchies
• Time Series
• Geographical Data
• Text Data

Required reading (pre-class)

• VAD book CH 3 – Why: Task Abstraction

In-class Agenda

• Why? Analyis Tasks: Actions and Targets
• Data and Task Abstraction: A Four-Level Analysis Framework
• Exercise / Live-Demo

Further reading

• [The Nested Model] A Nested Model for Visualization Design and Validation. Tamara Munzner, 2009, https://doi.org/10.1109/TVCG.2009.111
• [Data and Tasks]: The eyes have it: A task by data type taxonomy for information visualizations - B. Shneiderman - 1996

Recommended reading (pre-class)

• no entry

In-class Agenda

• Introduction - About Patterns and Models
• Knowledge Generation - Patterns, Models, and Analytical Reasoning
• Data Transform. Processes - The InfoVis and the KDD Process
• Humans and Machines - Strengths and Weaknesses
• Visual Analytics - Synthesis
• Exercise / Live-Demo

Further Reading

• Book: Illuminating the Path: The Research and Development Agenda for Visual Analytics. Thomas, J. and Cook, K. National Visualization and Analytics Center, 2005
• Paper:Visual Analytics: Definition, Process, and Challenges. D Keim, G Andrienko, JD Fekete, C Gorg, J Kohlhammer, G Melancon, 2008

In-class Agenda

• Introduction - About Patterns and Models
• Knowledge Generation - Patterns, Models, and Analytical Reasoning
• Data Transform. Processes - The InfoVis and the KDD Process
• Humans and Machines - Strengths and Weaknesses
• Visual Analytics - Synthesis
• Exercise / Live-Demo

In-class Agenda

• Introduction - About Patterns and Models
• Knowledge Generation - Patterns, Models, and Analytical Reasoning
• Data Transform. Processes - The InfoVis and the KDD Process
• Humans and Machines - Strengths and Weaknesses
• Visual Analytics - Synthesis
• Exercise / Live-Demo

In-class Agenda

• Aspects of Dirty Data: Identification and Curation
• Data Transformations: Making data usable and useful
• Visual Preprocessing: Examples of VIS tool usage in applications
• Exercise / Live-Demo

Further Reading

• [KH*2011] Kandel S, Heer J, Plaisant C, Kennedy J, Van Ham F, Riche NH, Weaver C, Lee B, Brodbeck D, Buono P. Research directions in data wrangling: Visualizations and transformations for usable and credible data. Information Visualization. 2011. https://doi.org/10.1177/1473871611415994

In-class Agenda

• Unsupervised ML: The two "Work Horses"
• Clustering: Finding Groups in Datasets
• Dimensionality Reduction: Reducing the Number of Attributes
• Visual Data Exploration: Using Self-Organizing Maps (SOM)
• Data Exploration Application: Interactive Exploration of Funds Data
• Exercise / Live-Demo

Recommended reading (pre-class)

• Paper: Hohman F, Kahng M, Pienta R, Chau DH. Visual analytics in deep learning: An interrogative survey for the next frontiers. IEEE Transactions on Visualization and Computer Graphics (TVCG). 2018. DOI: 10.1109/TVCG.2018.2843369 URL: https://ieeexplore.ieee.org/abstract/document/8371286

In-class Agenda

• Supervised ML Special: The two "Work Horses"
• Classification and Regression: "Predicting categories and numbers"
• Explainable AI Special: Why something happens in ML models
• Exercise / Live-Demo

In-class Agenda

• Detecting and Mitigating Bias: In (and with) Visual Analytics
• Exercise / Live-Demo

In-class Agenda

• HCAI: Ensuring human values and control, while increasing automation

• HCAI for Sustainability: Live demo+activity: SDG Research Scout

• Exercise / Live-Demo

In-class Agenda

• Human Knowledge Externalization: Eliciting Semantic Information

• Human-Centered Data Labeling: Application examples and Live experience

• LLM-based “Knowledge” Externalization: Eliciting Semantic Information using LLMs/GPTs

• Exercise / Live-Demo

In-class Agenda

• Introduction: Preference-Based and Personalized Analytics

• Preference-based Analytics:

  • Creation of a personalized music classifier

  • Creation of a preference-based item ranking

  • Creation of a similarity metric for countries

  • Personalized analytics of Type-1-diabetes

• Exercise / Live-Demo