Navigation auf uzh.ch
Navigation auf uzh.ch
| Lecturer | Prof. Dr. Jürgen Bernard |
| TAs & Tutors | |
| Teaching Language | English |
| Level, ECTS |
MSc (6 ECTS) PhD (DSI) (2 ECTS) |
| Notes | If you have already taken the course „Introduction to Interactive-Visual Data Analysis (MINF4570)" in Fall 2021, you are not allowed to book this course. |
| Academic Semester | Fall 2023 |
| Time and Location |
Tuesday 16:15 - 18:00, Room BIN 0.K.02 Thursday 14:00 - 15:45, Rooms BIN 2.A.01 & BIN 2.A.10 |
| Digital Backups |
Slides will be on OLAT, additional course material will be as described below. There is no recording of the lecture and exercise, as this is an interactive course requiring live participation. |
| OLAT | To access all the course material OLAT |
| Start Date | 19.09.2023 |
| End Date | 21.12.2023 |
| Course Material |
Coursebook (Visualization Analysis and Design, Tamara Munzner) Research papers (as announced) |
| IVDA Programming Tutorial |
Available on GitLab |
| Prerequisites | Willingness to participate actively in class is recommended. Willingness to work in groups to face data analysis challenges together. |
| Prior Knowledge | Programming expertise is mandatory. 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. |
| Grading | Regular per-person exercises and individual homework assignments (50%), programming project in the group including iterative presentation and report submission (50%). Students must pass both the individual grade and the overall grade for successful completion of the course. |
| General Inquires | For any general inquires about the course send an email |
As of 2022, also applies to 2023.
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.
| L01 |
Introduction to IVDA
|
| L02 | What: dataset types and data attributes |
| L03 |
Why: analysis tasks and abstractions
|
| L04 | How: marks, channels, and visualization guidelines |
| L05 | How: interaction techniques and view composition |
| L06 | How: advanced visualization techniques |
| L07 | Introduction to Visual Analytics |
| L08 | Data wrangling and visual preprocessing |
|
L09 |
Unsupervised machine learning and data exploration |
| L10 | Supervised machine learning and data explanation |
| L11 |
Human-centered artificial intelligence
|
| L12 | Human knowledge externalization |
Interactive Demo: Exploration of Funds Data
What to analyze? Dataset Types, the between-objects perspective
What to analyze? Data Attributes: nominal, ordinal, and numerical
[Dataset Types]: 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
Why analyze? Analysis Tasks - actions and targets
Why analyze? Data and Task Abstraction using a four-level analysis framework for design and validation
[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
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
How analyze? Exercise
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
Advanced visualization techniques for...
Web-based overviews of techniques for...
J. Bernard, M. Zeppelzauer, M. Sedlmair, and W. Aigner: VIAL – A Unified Process for Visual-Interactive Labeling. The Visual Computer Journal (TVCJ), 2018
Further Reading
