Societal Computing

What it is about

You learn how to use human-centered data to discover patterns of societal behavior, test theories, and conduct responsible research. By the end of the course you will be able to perform data analyses, systematically plan research projects, and critically assess the societal implications of computing.

What you will be able to do

Understand fundamentals and current concepts in Societal Computing
Apply methodological and analytical skills for research
Solve problems from the societal context with data-driven methods
Perform data analyses and interpret results appropriately
Systematically plan and conduct a small-scale research project
Strengthen oral presentation and discussion skills

What the module consists of

Vorlesung mit integrierten ÜbungenIntroduction to topics, group discussions and exercises
Gruppen- und EinzelübungenPractical application, take-home and in-class tasks
Projektarbeit (mehrteilige Abgabe und Präsentation)Design, execution and documentation of a small research project

Teaching method

VorlesungConveying fundamentals, theories and current research results
integrative Übungen (Einzel/Gruppe)Practice problem solving and methodological skills
Literaturarbeit und DiskussionCritical engagement with research and preparation of tasks
blended Learning (Präsenz + Online-Inhalte)More flexible provision of content and preparatory materials

What you should already know

keine formalen Voraussetzungenrecommended

Assessment

ÜbungsabgabenTake-home-AufgabenPresentationProject reportmündliches Proposal

The examination consists of eight practical exercises, due roughly every two weeks (in-class and take-home). In addition, the assessment includes a four-part project (oral and written: Proposal, Midterm Report, Final Presentation, Final Report). Each exercise is graded out of 100 points. The cumulative performance of in-class and take-home exercises accounts for 60% of the grade (the worst exercise may be waived once if desired). The presentation accounts for 10%. The project accounts for 30% (Proposal 10%, Midterm Report 20%, Final Presentation 20%, Final Report 50%). All required submissions must be completed to pass the module; however, complete submissions do not automatically guarantee a passing module grade. Late or incomplete submissions will only be considered with prior agreement with the instructor or in case of emergency.

Reading list

David Lazer et al. - Computational Social Science
danah boyd & Kate Crawford - Critical questions for big data
David Lazer et al. - Computational social science: Obstacles and opportunities

Filters

Societal Computing

What it is about

What you will be able to do

What the module consists of

Teaching method

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