Citizenship in a post-private world

What are we going to gain by tracking behaviours?

MheatMap-0-2-0-M

We would like to introduce you to the amazing world of gathering data from social media, producing knowledge out of it  and sharing it with others- simply put: spinning trash to gold.

Just imagine the ideas a bunch of creative students could come up with, not necessarily connected to designing a building – more like adding some kind of intelligence to a city. This kind of crowd sourced knowledge is already out there and it is now up to you to create ways of how to communicate with it. A real-time heat map of flu pandemics in February so you know which tramlines not to use, a twitterbot updating you about party hotspots or which clubs to avoid,  some kind of online service telling you where the fewest people are swimming so you can enjoy doing sports a little more, …

In this Wahlfach we will learn to harvest and refine a dynamic interactive stream of data – connecting you to your city the way you want it. There will be an introduction to programming with the framework Processing and and to simple Data Mining, as well as on demand introductions to different social media APIs. Coding skills are useful but not mandatory. All of you who are interested in this topic clearly do not need to know how to code. It is arguable that learning-by-doing is the best way to start coding, just be aware that being motivated is essential.

number max. 20 / min. 10 motivated students
dates Mondays, 13:00 – 15:00
introduction 23.09.2013
place Chair for CAAD, HPZ, Floor F unless announced differently
tutors Matthias Standfest, Mathias Bernhard, Knut Brunier

THE COURSE STRUCTURE
This course is weighted with 2 creditpoints, which leads to a workload of 60 hours in total. These hours are split into 20h for the attendance (while 4 of those are supposed to focus on the individual seminar work in two workshops), 10h preparation for the courses and another 30h for the seminar work.

THE COURSE FOCUS
Skills taught in this course are: reading code and gaining the necessary understanding to customize it, classical and modern statistics (eg standard deviation, Pagerank, SOM, …), visualizing Statistics (eG Heat Maps), interfaces to collect data (eg Twitter, Google,..).

THE TIMETABLE
Additional to each lesson we prepared Tutorials and the teaching Team is always easy-to-reach by email or during office hours on prior call. The timetable for this semester is set up following way:

 Date Content To prepare

23.09.2013

Intro

30.09.2013

Techn. Intro Processing Read Linklist

07.10.2013

Twitter API + Processing Processing Example

14.10.2013

Google API and Diskussion Brainstorming Seminar Work

21.10.2013

No course due to seminar week

28.10.2013

Statistics 101 Statistics Example

04.11.2013

Visualization 101 Visualization Example

11.11.2013

Statistics 102 Concept Seminar Work

18.11.2013

Workshop Seminar Work (Vis, Stat, API) Finalization Seminar Work

25.11.2013

Workshop Seminar Work (Vis, Stat, API) Finalization Seminar Work

02.12.2013

Final Presentation and Critiques

ADDITIONAL READINGS

http://en.wikipedia.org/wiki/Big_data
http://en.wikipedia.org/wiki/Social_data_revolution
http://en.wikipedia.org/wiki/Google_Flu_Trends
http://www.wired.com/science/discoveries/magazine/16-07/pb_theory
http://norvig.com/chomsky.html
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.4933
http://en.wikipedia.org/wiki/PRISM_%28surveillance_program%29
http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/
http://www.golem.de/1201/89133.html
http://quantifiedself.com/
http://quantifiedself.com/guide/
http://trendsmap.com/
http://www.faz.net/aktuell/feuilleton/modelle-die-sich-schlecht-benehmen/f-a-z-column-by-emanuel-derman-little-big-data-12103958.html