Module Overview
...
Benjamin Wiederkehr
benjamin@interactivethings.com
076 / +41 76 533 33 72
Data analysis, visualization, interaction, narration, communication, and evaluation
...
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
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
09.30-17.00
4.D12, TG Introduction From Data to Knowledge Data Sources Please bring your own laptop to the course! | Data Literacy
09.30-17.00
4.D12, TG 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
09.30-17.00
4.D12, TG Data Quality More Data Tools
Geospatial Data
tbd. | Data Visualization
Introduction & Briefing 09.00-12.0
4.D12, BW, JG Topic and Data Research | Design Input 1
Basic Techniques 09.00-12.00
4.D12, BW Topic and Data Research | Tech Input I 09.00-12.00
4.D12, JG Mentoring
13.00-15.00
Atelier, BW, JG 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
Intermediary Techniques 09.00-12.00
4.D12, BW Mentoring
13.00-15.00
Atelier, BW, JG Concept | Tech Input 2 09.00-12.00
4.D12, JG Concept Finalization | Aesthetics of Interaction
09.00 - 12.00 Production | Mentoring
09.00-12.00
Atelier, BW, JG Production |
Week 5 | Tuesday 28.11 | Wednesday 29.11 | Thursday 30.11 | Friday 1.12 |
|---|
B&A | Production | Aesthetics of Interaction
09.00 - 12.00 Mentoring
13.00-17.00
Atelier, BW, JG Production | Production | Presentation
09.00-12.00
4.D12, BW, JG Documentation
|
TG: Timo Grossenbacher, BW: Benjamin Wiederkehr, JG: Joël Gähwiler
Inputs
Graphic PropertiesBW- Human Properties
- Graphical Encoding
- Graphical Methods
|
Graphical ExcellenceExport => IllustratorProgramming- Programmatic Analysis
- Programmatic Transformation
| JG |
|
TG: Timo Grossenbacher, BW: Benjamin Wiederkehr, JG: Joël Gähwiler
Module Materials
Data
Books
...
Websites
Podcasts
- Data Stories by Moritz Stefaner and Enrico Bertini
- Policy Viz by Jon Schwabish
- Especially Big Data by Fathom
- Data Skeptic by Kyle Polich
Videos
Articles
On ethics:
...
On color:
On interactivity:
...
Tools
A variety of useful toolkits have been designed to help support information visualization applications. Some include support for the full visualization pipeline from data to interactive graphics, while others focus only on a subset, typically graphics and interaction.
Visualization Cheatsheets
...
Visualization Toolkits
...
Visualization Tools
...
Visualization Programming Environments
- Bl.ocks: Code examples, hosting, and reviews
- Bl.ocks Explorer: Explore and discover bl.ocks based on D3 API calls
- Block Builder: Environment to create, edit, and fork D3 examples
Network Analysis Tools
...
Color Tools
...
Data Literacy Resources
Here are the slides for this part of the course. Also have a look at the speaker notes, sometimes there's a bit more information.
General / good reads
Data quality
- The Quartz Guide To Bad Data
- Tidy Data – although the principle of tidy data stems from an R developer and the examples in this document are made in R, "tidy data" is a very valuable standard that you should achieve when working with data. Once your data is "tidy", visualization in R (or in any other language / framework) becomes easier. You can also look at the more formal and less R-heavy scientific paper.
Data formats / conversions
- http://www.convertcsv.com for converting data from a myriad of formats into others, e.g. from CSV to JSON.
- http://www.reformattext.com for formatting text files and extracting information out of them.
- https://jsonformatter.curiousconcept.com/ for validating and formatting JSON data.
- An introduction to APIs
- How to use APIs with Python – The normal way of working with APIs is via a programming language. Python lends itself to communicating with many APIs, so once you know a bit of Python, this interactive course might be good for you.
Regex
- https://regexr.com/: Interactively test and learn Regex
- This tutorial doesn't look too bad: Learn Regex the Easy Way
Geospatial data
- An overview of common spatial data formats
- QGIS is an open source geographical information system and a good option for working with geodata on a non-regular basis (e.g. to look at some data or to compute areas of some regions). This tutorial collection will introduce you to it.
- geojson.io can be used to quickly visualize GeoJSON. Mapshaper can be used to generalize geometries and reduce file size, and it can convert between different file formats.
- ogr2ogr (and ogrinfo) are two very helpful CLI tools – just google for some tutorials!
- https://tools.retorte.ch/map/: Convert coordinates into/from the Swiss projection system.
- Municipal boundaries and districts of Swiss cities by the BFS. Jeremy Stucki (former IAD student) has processed these and other data into TopoJSON (easier to use for web applications).
Working with the CLI
The command line interface (CLI, often referred to as "shell" or "terminal") is a very powerful tool, and each operating system has one. Working with the CLI is easiest on Linux and Mac, and they are both similar since they are both based on Unix. I really recommend getting to know the 101 of working with the terminal, e.g. through this tutorial. Here are some more tips for working with data on the CLI. This Twitter account gives useful and sometimes funny tips on how to make the most of the terminal.
Data processing in R
...