Module Overview
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 sessions, in-class exercises, assignments and independent study blocks.
Projects are conducted in 5 groups of 3 students.
Module Details
- Course title: Interactive Visualization
- Dates: November 2 – December 1 2017
- Days: Tuesday to Friday
- Lecture hours: 09.30 – 17.00
- Office hours: 09.30 – 17.00
Module Instructors
Joël Gähwiler
joel.gaehwiler@zhdk.ch
Technology and programming
Benjamin Wiederkehr
benjamin@interactivethings.com
Data analysis, visualization, interaction, narration, communication, and evaluation
Timo Grossenbacher
timo@timogrossenbacher.ch
Module Sessions
Lectures
Presentations will introduce the students to the essential theory and practice of data literacy and data visualization.
Design Studio
Collective review sessions where the students can get and give feedback to their current state.
Coding Lab
Collaborative coding sessions where the students can experiment and get support.
Group Mentoring
Individual review and coaching sessions where the instructors give advice to groups of students.
Overview and Objectives
Topic Overview
...
Module Outline
The module is split into three parts:
Week 1: Data Literacy
Students learn the basics of data acquisition,
Week 2: Reading Week
Week 3 – 5: Data Visualization
Expectations and Grading
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.
- 40% Final work
- 20% Group discussions
- 20% Journal documentation
- 10% Class participation
Any assignment that remains unfulfilled receives a failing grade.
Deliverables
See Example below:
- Oral presentations
Students must independently prepare lectures on selected texts from the week. These can be presented in different formats.
Possible presentation formats are:
- Live sketching
- Demo with prototyping
- Slides presentation
- etc.
The presentation should include a 3-pages written discussion, made available to the class and instructor by Friday 9am, prior to the day of the class to insure a general discussion.
The paper should include title, author, date, context, summary, bibliography.
Additional sources can be added to inform the discussion if necessary.
- Final Essay
The essay is a final 2500-words essay with a diversity of sources and bibliography (classified by genre: book, book chapter, journal article, conference article, academic thesis, newspaper article, web article, etc).
The topic of the essay is chosen by the student and proposed by Week 8 in the form of a short paragraph (100 words) explaining the topic and the questions at stake. I will inform the student if the topic is accepted in that week. The final essay has to be submitted by Week 12.
The paper should be written in English.
- Journal/Blog
A separate 'Journal' is developed by each student that reflects on learnings from the seminar. It should be in the form of an online blog (ie. WordPress, Tumblr or other):
- The journal should be structured in a generally comprehensible manner
- The lecture notes, including annotations, are stored
- Notes, sketches for each lesson should be included as well
Course Materials
Essentials
Add a short list of specific articles, chapters, videos, podcasts, etc. here.
Books
...
- https://www.vis4.net/blog/posts/mastering-multi-hued-color-scales/
- https://www.vis4.net/blog/posts/avoid-equidistant-hsv-colors/
- https://blog.graphiq.com/finding-the-right-color-palettes-for-data-visualizations-fcd4e707a283
- Interactive or not? https://www.fastcodesign.com/3069008/the-problem-with-interactive-graphics and https://www.vis4.net/blog/posts/in-defense-of-interactive-graphics/ and https://medium.com/@dominikus/static-visualizations-do-not-exist-b2b8de1ed224
- The Quartz Guide To Bad Data: https://github.com/Quartz/bad-data-guide
- Data Stories by Moritz Stefaner and Enrico Bertini
Tools
- http://datavizproject.com/
- http://ft-interactive.github.io/visual-vocabulary/
- http://labs.juiceanalytics.com/chartchooser/index.html
- https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf - a cheatsheet for ggplot2 (see below)
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Calendar
Freigeben: 28.11, 30.11, Nachmittage
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Week 1
...
Tuesday 31.10
...
Wednesday 1.11
...
Thursday 2.11
...
Friday 3.11
...
Data Literacy
09.30-17.00
4.D12, TG
...
Data Literacy
09.30-17.00
4.D12, TG
...
Week 2
...
Tuesday 7.11
...
Wednesday 8.11
...
Thursday 9.11
...
Friday 10.11
...
Data Literacy
09.30-17.00
4.D12, TG
...
Kick Off Course
09.00-12.0
4.D12, BW, JG
...
Module Overview
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.
Module Details
- 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
Module Theme
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.
Module Instructors
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
Module Sessions
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.
Subject and Objectives
Subject Overview
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.
Module Outline
The module is split into three parts:
Week 1: Data Literacy
Students learn the basics of data acquisition,
Week 2: Reading Week
Week 3 – 5: Data Visualization
Assignment
Deliverables
Graphical Poster
Interactive Prototype
Documentation
Grading
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.
Calendar
Week 1 | Tuesday 31.10 | Wednesday 1.11 | Thursday 2.11 | Friday 3.11 | |
---|---|---|---|---|---|
Data Literacy Introduction From Data to Knowledge Data Sources | Data Literacy Data Sources & Quality Data Types / Formats Data Tools / Working with Data | ||||
Week 2 | Tuesday 7.11 | Wednesday 8.11 | Thursday 9.11 | Friday 10.11 | |
Data Literacy Data Quality More Data Tools Geospatial Data tbd. | Data Visualization 09.00-12.0 Topic and Data Research | Design Input 1 09.00-12.00 Topic and Data Research | Tech Input I 09.00-12.00 Mentoring Topic and Data Research | ||
Week 3 | Tuesday 14.11 | Wednesday 15.11 | Thursday 16.11 | Friday 17.11 | |
B&A | Data Analysis | Data Analysis | Ideation and Concept | Ideation and Concept | |
Week 4 | Tuesday 21.11 | Wednesday 22.11 | Thursday 23.11 | Friday 24.11 | |
B&A | Design Input 2 09.00-12.00 Mentoring Concept | Tech Input 2 09.00-12.00 Concept Finalization | Aesthetics of Interaction Production | Mentoring Production | |
Week 5 | Tuesday 28.11 | Wednesday 29.11 | Thursday 30.11 | Friday 1.12 | |
B&A | Production | Aesthetics of Interaction Mentoring Production | Production | Presentation | Input 2 Mentoring Analysis |
Week 3 | Tuesday 14.11 | Wednesday 15.11 | Thursday 16.11 | Friday 17.11 | Analysis | Analysis | Concept | Concept |
Week 4 | Tuesday 21.11 | Wednesday 22.11 | Thursday 23.11 | Friday 24.11 | Input 3 Mentoring | Input 4 Concept | Aesthetics of Interation Concept | Mentoring Production |
Week 5 | Tuesday 28.11 | Wednesday 29.11 | Thursday 30.11 | Friday 1.12 | |
Production | Aesthetics of Interation Mentoring | Production | Final Presentation Documentation, BW, JG Documentation |
TG: Timo Grossenbacher, BW: Benjamin Wiederkehr, JG: Joël Gähwiler
Inputs
Input | Date | Content | Instructor | Slides |
---|---|---|---|---|
Introduction & Briefing | 8.11 | Data Visualization Foundation
Briefing
| BW | Interactive Visualization — Data Visualization Foundation — Benjamin Wiederkehr (2017).pdf |
Design Input 1 | 9.11 | Basic Techniques
| BW | |
Design Input 2 | 21.11 | Intermediary Techniques
| BW | Interactive Visualization — Intermediary Techniques — Benjamin Wiederkehr (2017).pdf |
Technology Input 1 | 10.11 |
| JG | |
Technology Input 2 | 22.11 |
| JG |
TG: Timo Grossenbacher, BW: Benjamin Wiederkehr, JG: Joël Gähwiler