Coursebooks

Data Think! (SwissUniversities)

AR-805

Lecturer(s) :

Koseki Shin Alexandre
Lecturers (see below)
Mazel-Cabasse Charlotte Julie Sophie

Language:

English

Frequency

Only this year

Remark

February 15th - 19th 2021

Summary

Data Think! offers theoretical and practical introduction to data-centric research. Participants learn about collaborative research pipelines, data management plans, data ethics, data visualization and data valorisation strategies. We focus on the use of social, urban and environmental data.

Content

Our world - scientific and social - revolves increasingly around the notion of "data". In this context, the capability to think in terms of data plays an increasingly central role for many activities with research and the industry. Researchers now need not only to manage their own research data, they must know how to design and maintain efficient and innovative "data cycles" according the guiding principles of "Open Science".

Data Think! provides a conceptual, technical and practical understanding of research data within today's "data centric" context. Throughout this block-course, participants acquire knowlege and skills that prepare them to assess, manage, analyze and visualize different research data: first-hand data, second-hand data, qualitative, quantitative, Big Data, etc. In an innovative format, Data Think! proposes a critically informed hands-on approach to investigate the potential, the limitation and the risks associated with data in interdisciplinary and transdisciplinary research.

Data Think! takes place over five consecutive days (Monday to Friday). With the help of lecturers and experts, participants explore the use of data in research protocols, from design to archiving. In order to improve research skills and computational thinking, participants work directly with Jupyter Notebooks and Github. Lectures and group debates complement in-class exercices and collaborative workshops where participants learn to build a digital research pipeline centered on their data cycle in the spirit of Open Science and Open Data.

This international graduate block course is produced by the dhCenter UNIL-EPFL in collaboration with the EPFL PhD program in Architecture and Sciences of the city (EDAR), EPFL PhD program in Digital Humanities (EDDH) and UNIL PhD program in Digital Studies (EDEN), in collaboration with the MPG-UZH Center for Digital Visual Studies (UZH-Max Planck Society) and with support from the EPFL NOTO project.

For more information, contact Dr Shin Koseki: shin.koseki@epfl.ch / shin.koseki@unil.ch

 

Note

This course offers an introductory and intermediary overview of data ethics, management, analysis and visualization for students in social sciences, humanities and design. Quantitative and qualitative approaches are welcome.

Keywords

Data, Data Management, Data Ethics, Data Visualization, Open Data, Jupyter Notebook, Python, Research pipeline, Open Science

Learning Prerequisites

Important concepts to start the course

Complete the pre-course assignments.

Learning Outcomes

By the end of the course, the student must be able to:

Resources

Bibliography

Kitchen, Rob. 2014. *The Data Revolution* ; Taylor, Linnet. 2017. "What is Data Justice" in *Big Data & Society* ; Wilkinson, Mark et al. 2016. "The FAIR guiding principles..." in *Nature*. Lazer, David, et al. 2009. "Computational Social Science." In *Science* ; jupyter.org ; github.com ; gitlab.com ; renkulab.io.

Ressources en bibliothèque

In the programs

Reference week

 
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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German