Syllabus Example: the Data Inquiry Course

This course explores the consequences of the digital traceability of collective phenomena with a critical and empirical approach. Considering a variety of computational methods, it offers first-hand experience of digital quantification, examining its potential, but also its shortcomings and biases. Students will learn techniques of data collection, corpus cleaning, exploratory analysis, network analysis, natural language processing, artificial intelligence, and information visualization. Working in groups, they will apply these techniques to actual societal situations. Through this experience, they will be led to consider reflexively the insights of the transdisciplinary field of critical data studies.

Course Structure
Pedagogical Approach
Learning Outcomes
Class Programme

Course structure

The course is composed of three main parts:

  1. Critical theories of quantification, digital and online media.
  2. Digital, computational and qualitative-quantitative methods.
  3. Case study group work.

The version of the course presented in this page grants equal space to the three parts, but each can be expanded according to the curriculum in which the course is integrated.

Pedagogical Approach

While data literacy is increasingly considered as an essential skill in a variety of curricula, its teaching is often unfit to prepare students for their future occupations. Trained on predefined exercises and artificial datasets, students are rarely exposed to the messiness of real data practice. They assimilate techniques of digital analysis and visualization, but fail to learn the subtler craft of thinking about and with data. Focussing too much on data crunching and not enough on the conditions of the production of data and on the consequences of their use, this type of training discourages reflexivity and encourages a naively technical approach to data literacy. Students are rarely brought to reflect on the work necessary to distill datasets from social and natural phenomena and even less on the (side)effects of managing those phenomena through their data-doubles. To promote a richer and more realistic approach to “data literacy”, this course draws on the literature from disciplines such as science and technology studies, media studies, political sciences, security studies etc. Data Inquiries, however, does not deliver these insights through a conceptual instruction, but rather through group-work on real projects developed by civil organizations working with data.

Learning Outcomes

  1. Explore a variety of different digital techniques for collecting, cleaning, analyzing and visualizing data. Be able to compare the strengths and weaknesses of the different conceptual, mathematical and technical tools and to choose the ones that suit best the resources and objectives of one’s project.
  2. Develop the ability to adapt to the constant evolution of computational technologies. Know how to update one’s repertoire of technical and conceptual tools and learn how to find new software, scripts, libraries and how to adapt them to one’s needs.
  3. Become familiar with all the stages of a data inquiry and learn to manage such a project from start to finish, by effectively linking different tools and dealing with the accumulation of transformations and possible distortions that this implies.
  4. Develop a critical reflection on the impacts of the increasing quantification of social life promoted by digital technologies. Learn to appreciate its potential for scientific research, public debate and the management of communities and businesses, but also to recognize its possible biases and its economic and political consequences.
  5. Know how to work in groups and in collaboration with societal actors. Know how to organize a data project as a collective and participatory effort. Work in cooperation with other students, but also with stakeholders inside and outside the academy.

Class Programme

Part I - Theories of quantification, digital and online media

Class 1 - Introduction

Class 2 - “An Engine not a Camera”, the consequences of categorization and quantification

Class 3 - Raw data is an oxymoron

Class 4 - The platforms and their problems

Class 5 - Quantifying differently: data activism

Part II - Digital, computational and qualitative-quantitative methods

Class 6 - Data collection by querying, scraping and crawling

Class 7 - Cleaning digital records and building a data corpus

Class 8 - Exploratory data analysis

Class 9 - Data visualization and data story-telling

Class 10 - Mathematical and visual analysis of networks

Class 11 - Introduction to Machine Learning

Part III - Case studies and group work

Optional troubleshooting class

Class 12 - Interpreting your results (without overinterpreting them)

Class 13 - Enough is enough! Finalize the story of your data inquiry

Class 14 - Transforming your data inquiry into a public intervention

####Presentation of the results of group projects In this class, the students will present the results of their investigation and their strategy to make them public and actionable in society. Civil society actors concerned by the projects will be invited to participate in discussion.


The course grade is the average of the three grades received by students in each part of the course:

  1. The evaluation for the first part of the course is an individual grade. Students are evaluated on their understanding of the texts (60%) and their engagement in the discussion (40%).
  2. The evaluation for the second part is a collective grade on the various statistical and visual analyses produced by each group. Groups are evaluated for their punctuality in handing in the various exercises (40%) and by the quality and richness of their results (60%).
  3. The evaluation for the third part of the course is a collective grade on the data inquiry carried out by each group. Groups are evaluated on the overall quality of their results (33%), the ability to present them in a coherent narrative (33%) and on their ability to make them public and socially impactful (33%).