Module Overview
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Data Literacy Resources
General / good reads
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Data formats / conversions
Regex
- https://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.
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
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.
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.
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Data journalism examples
Calendar
Week 1 | Tuesday 31.10 | Wednesday 1.11 | Thursday 2.11 | Friday 3.11 |
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| 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
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Week 2 | Tuesday 7.11 | Wednesday 8.11 | Thursday 9.11 | Friday 10.11 |
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| Data Literacy
09.30-17.00
4.D12, TG Data Quality More Data Tools
Geospatial Data
tbd. | Data Visualization
Introduction & Brief 09.00-12.0
4.D12, BW, JG Research | Design Input 1
Basic Techniques 09.00-12.00
4.D12, BW Research | Tech Input I 09.00-12.00
4.D12, JG Mentoring
13.00-15.00
Atelier, BW, JG Analysis |
Week 3 | Tuesday 14.11 | Wednesday 15.11 | Thursday 16.11 | Friday 17.11 |
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| Analysis | Analysis | Concept | Concept |
Week 4 | Tuesday 21.11 | Wednesday 22.11 | Thursday 23.11 | Friday 24.11 |
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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 | Aesthetics of Interaction
09.00 - 12.00 Concept | Mentoring
09.00-12.00
Atelier, BW, JG Production |
Week 5 | Tuesday 28.11 | Wednesday 29.11 | Thursday 30.11 | Friday 1.12 |
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B&A | Production | Aesthetics of Interaction
09.00 - 12.00 Mentoring
13.00-17.00
Atelier, BW, JG Production | Production | Final Presentation
09.00-12.00
4.D12, BW, JG Documentation
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TG: Timo Grossenbacher, BW: Benjamin Wiederkehr, JG: Joël Gähwiler
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