Interactive Visualization HS 2017

The module takes place over 5 weeks, including a reading week (3), from Tuesday to Friday, 9.30 – 17.00, November 02 – December 01 2017. Class sessions include lectures, discussions, mentoring, in-class exercises, project assignments and independent study blocks. Project assignments are conducted in 5 groups of 3 students.

• Title: Interactive Visualization

• Dates: November 2 – December 1 2017

• Days: Tuesday to Friday

• Lecture hours: 09.00 – 17.00

• Office hours: 09.00 – 17.00

• Language: English

The theme of this module is aligned with the overall theme of the fall semester 2017: instability. We interpret the term instability in the context of this course as an antonym to stability and the students are asked to look for stability, instability, or the dynamic between these two terms in data sets covering the world around us. Tectonic instability leading to earthquakes, job instability leading to fluctuating jobless rates, economic instability leading to financial crises, political instability leading to democratic overhaul, are just some of the potential examples.

Joël Gähwiler
joel.gaehwiler@zhdk.ch
Data visualization tools and technology

Benjamin Wiederkehr
benjamin@interactivethings.com
+41 76 533 33 72
Data analysis, visualization, interaction, narration, communication, and evaluation

Timo Grossenbacher
timo@timogrossenbacher.ch
Data sources and data quality, data acquisition, formatting and converting data.

Input Lectures
Presentations will introduce the students to the essential theory and practice of data literacy and data visualization. These lectures will be divided into design- and technology-oriented inputs. Joël Gähwiler will provide the technology inputs, Benjamin Wiederkehr will provide the design inputs.

Group Mentoring
Individual review and coaching sessions where the instructors give advice to groups of students.

Many aspects of society, science, business, finance, journalism, and everyday human activity, become ever more quantified. As a result, our world is awash with data of increasing amount and complexity. Still, we must keep afloat with our innate human abilities and limitations. For designers in this environment, working confidently with data becomes an essential skill. Visualization is one way to tame this information overload: well-designed representations replace difficult cognitive calculations with simpler perceptual interpretations. They can thus improve accessibility, comprehension, and memory. More literally, visualization is the process of transforming data into visuals like charts, graphs, and maps. These are then used to explore, evaluate and explain insights hidden in the data. The goal being to engage and aid diverse audiences in analytical sense and decision making.

This course provides students with an introduction into the theory and practice of designing with data while keeping the human in mind. They learn the basics for creating effective data visualizations. This includes principles from graphic design, human-computer interaction, perceptual psychology, cognitive science, and statistics. We touch on the topics of data literacy, visual perception, graphical encoding, visualization types, color, interaction, animation, exploration, and explanation. In a practical assignment, students apply the techniques, tools, and technologies to design and develop interactive visualizations. After this course, students will be able to turn a data source into a useful, truthful, and beautiful data experience — tailored to specific information needs or communication goals.

The module is split into three parts:

Week 1: Data Literacy
Students learn the basics of data acquisition, formatting, and data conversions. They get to know a wide variety of data sources.

Week 2: Reading Week
Students conduct personal research, read articles, watch videos, and listen to podcasts relevant to the topic of data visualization.

Week 3 – 5: Data Visualization
Students learn and apply the basics of data visualization within a group assignment to build their own interactive visualization.

The students collaborate in groups of three people. We recommend to form cross-functional groups where technically-oriented people team up with design-oriented people. We propose the following process to achieve the expected results in time.

1. In the first phase, the discovery phase, the students explore indicators of instability in the world around us. Such indicators can be found in economics, politics, or finances, but also in social diversity or climate phenomena. The students will research data sets which hold the potential to describe or even explain this instability.

2. In the second phase, the definition phase, the students will conduct a visual analysis of the selected data sets to find their key insight. This can include visualizing different facets of a data set, visualizing a data set with different graphical methods, or putting different data sets in relation to each other. The students will define a question which they can answer using the right data set and the right visualization technique.

3. In the third phase, the development phase, the students design and develop their visualization into a final visual artifact. This is either a graphical poster (minimum requirement) or an interactive prototype (advanced requirement). Depending on the student composition and the intended reader / user experience, the priority between these two artifacts can be defined for each group individually.

4. In the fourth phase, the delivery phase, the students will exhibit and present their work to the rest of the class. Besides the final result, we’re interested in the overall development process from interest to question to answer. These stages should be documented as a visual journal including sketches, mockups, and prototypes.

• Graphical Poster and Interactive Prototype

• Documentation as PDF

• Documentation for the web (text, images, videos)

We expect all groups to deliver and present at least a static data visualization of an appropriately sized data set which is relevant to the overarching topic of instability. The visualization should make the data set accessible to people not familiar with the subject matter and highlight important aspects like trends, patterns, or outliers. The poster should include, but is not limited to, the following content: Title, subtitle, introduction, visualization with scales, legends, and annotations, as well as methodology, sources, credits. Format: A3 Portrait

More advanced groups are asked to deliver a prototype of an interactive version of the visualization represented on the poster. The prototype should illustrate the intended functionality in terms of interaction and animation. The prototype can be built using any of, but not limited to, the following tools and technologies: InVision, Principle, Adobe After Effects, Adobe Animate, HTML, CSS, JS, etc. Format: Clickable Prototype

The documentation should include:

• Abstract

• Introduction into the topic and research question

• Data analysis with sources and methodology

• Design and development process with stages and variations

• Result with explanation and evaluation

• Conclusion and outlook

Grades will be based on group presentations, class participation, home assignments, documentation (journal) and final work. Contributing to constructive group feedback is an essential aspect of class participation. Regular attendance is required. Two or more unexcused absences will affect the final grade. Arriving late on more than one occasion will also affect the grade.

• 10% Participation (Data Literacy)

• 60% Final work (Data Visualization)

• 30% Documentation (Data Visualization)

Any assignment that remains unfulfilled receives a failing grade.

TG: Timo Grossenbacher, BW: Benjamin Wiederkehr, JG: Joël Gähwiler

TG: Timo Grossenbacher, BW: Benjamin Wiederkehr, JG: Joël Gähwiler