Accueil > Advanced quantitative methods

OGLM 2050 - Advanced Quantitative Methods

Type d'enseignement : Workshop

Semester : Autumn 2017-2018

Number of hours : 12

Language of tuition : English

Pre-requisite

Introductory Statistics.

Course Description

This course offers a critical analysis of how urban-level data are produced, interpreted, and, increasingly, released to the public. In order to get a practical understanding of these issues, students will visit a major data infrastructure, build an open data online census for selected cities from all continents, and study the political implications of data production through case studies. This workshop is run along the “Advanced Quantitative Methods” course, which enables students to build on their existing statistical training by mixing qualitative and quantitative insights. By the end of the course, students will be able to assess the quality of existing open data resources, critically discuss their political implications, and produce their own analysis of real-world public datasets.

Teachers

JARDIN, Antoine A. (PHD candidate, CEE - Sciences Po)

Pedagogical format

6 sessions of 2 hours (for each workshop). The course is organized as interactive workshops, which will include some practice with statistical software and online tools.

Course validation

Students will be required to learn how to operate the Open Data Census software developed by the Open Knowledge Foundation, and to write short paragraphs about their experience with it. Students will also be required to use the R statistical software. No prior knowledge of either tool is expected.

Required reading

  • Alain Desrosières, “Statistics and Social Critique”, Partecipazione e Conflitto 7(2): 348–359, 2014
  • Data Journalism Handbook http://datajournalismhandbook.org/1.0/en
  • Rob Kitchin, The Data Revolution: Big Data, Open Data, Data Infrastructures and their Consequences, 2014

Additional required reading

  • Jon Agar, The Government Machine, 2003
  • Alain Desrosières, The Politics of Large Numbers, 1993
  • Lisa Gitelman, ‘Raw Data' Is An Oxymoron, 2013
  • Morten Jerven, Poor Numbers, 2013
  • James C. Scott, Seeing Like A State, 1990
  • Open Knowledge Foundation, The Open Data Handbook, 2012