Besides the course slides, the following reading materials are provided. These are important in providing additional details concerning visualization methods, techniques, discussions, and applications, beyond the slides. These are referred to at various points during the lecture. As such, make sure to follow the lectures, and also consult the footnotes in the lecture slides.
The course does not have a mandatory book, as all the required study information is present in the slides and reading materials listed here online. However, for the students interested to either study Visual Analytics in more depth, or have the materials in a more compact form, we recommend the following books:
- Visualization Analysis and Design (T. Munzner, CRC Press, 2014)
A very good book on general topics concerning information visualization, with a strong focus on the process of visual(ization) design and the visual encoding of information.
- Information Visualization: Perception for Design (C. Ware, Morgan Kaufman, 2012)
A complementary book to Munzner's infovis book. Covers additional topics on the perception of visual information and discusses a wider class of techniques.
- The Visual Display of Quantitative Information (E. Tufte, Graphics Press, 2001)
One of the so-called bibles of information visualization. This classical book covers deeply various topics into the visual encoding of information, with a focus on graphs and charts, and minimal visualization design. Also provides a rich incursion into the history of information visualization, starting before the computer era.
- Show Me the Numbers - Designing Tables and Graphs (S. Few, Analytic Press, 2012)
An easier read than the above books, with a practical hands-on focus on designing graphs and charts using mainstream tools such as e.g. Excel, Google Charts, or Tableau. Less focus on research-grade techniques.
- Data Visualization: A Successful Design Process (A. Kirk, Packt Publishing, 2012)
Similar in aims and scope to the previous book. Focuses on a hands-on explanation of the do's and dont's in designing high-impact visualizations. Less focus on research-grade techniques.
- Data Visualization - Principles and Practice (A. Telea, CRC Press, 2014)
While this book focuses mainly on the visualization of spatial data (a.k.a. scientific visualization), it contains a significant chapter that focuses on information visualization and visual analytics. This book is mainly aimed at implementors of visualization techniques and algorithms rather than designers.
- Principal Component Analysis (I. Jolliffe, 2nd edition, Springer, 2002)
This is an excellent and broad introduction to the meaning, computation, and applications of principal components, which form the basis of numerous multidimensional analysis and visualization techniques. The book covers principal component definitions, computation methods, visualization methods, dimensionality reduction, pattern detection and selection, and applications. The book is useful for both those interested in understanding existing visual analytics techniques, but also for those interested in implementing such techniques.
Below is listed a collection of articles and book chapters that focus on techniques, applications, and analyses discussed during the lecture. None of these articles is specific to a (narrow) class of techniques, applications, or use-cases. They all treat topics relevant for virtually any infovis design, and are hence important and valuable for the course.
Artistic Data Visualization (Viegas et al., 2007)
In Favor of Chart Junk (Baterman et al., 2010)
Guidelines for Good Graphics (Simon, 2016)
Is There Science in Visualization? (Jankun-Kelly et al., 2006)
Nested Model of Visualization Design and Validation (Munzner 2009)
On the Death of Visualization (Lorensen 2004)
Rainbow Colormap Still Considered Harmful (Borland et al., 2007)
The Chartjunk Debate (Few, 2011)
The Eyes Have It (Shneiderman 1996)
Understanding Visualization - A Formal Approach (Vickers 2013)
Value of Visualization (Van Wijk, 2005)
Below is a list of excerpts (chapters) from seminal books in Visual Analytics and Information Visualization. These offer higher-level, more generic, less application-specific, and thus easier to read and overall more applicable information than the articles above. Moreover, they offer a comprehensive collection of examples of visual analytics and information visualization applications in the real world.
Envisioning Information (Tufte, 1990)
Visual Display of Quantitative Information (Tufte, 1991 - excerpt 1)
Visual Display of Quantitative Information (Tufte, 1991 - excerpt 2)
Visual Display of Quantitative Information (Tufte, 1991 - excerpt 3)
Note: You can find the full books of which excerpts are given above easily on various sites, such as Google Books and Amazon.
Although the course is not currently ended by a written exam, it can be useful to check how well you understood various concepts. For this, try to answer these multiple-choice questions. Students who understood the presented material well should be able to get a score around 80% correct answers in roughly 30 minutes. To check if your answers were correct, see the detailed answers here.