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Lectures Year : 2017

Python for data science

Abstract

COURSE OBJECTIVE Political scientists and other researchers in the social sciences must often build and analyse datasets in the course of their work, which requires the use of tools, methods and processes they are usually not familiar with. This week-long summer school aims to introduce students and scholars to the techniques and practices required to build datasets for social science research (politics in particular) through classroom sessions and practical modules taught jointly by computer scientists and social scientists. The summer school will cover various aspects of data-based research, including data gathering, cleaning, management, analysis, and visualization. Working with map-based data and the challenges of combining quantitative and qualitative data will also be discussed. Under the mentorship of the instructors, participants will identify and work on their own mini-project during the summer school. They will work in teams to carry out data gathering, cleaning, analysis, visualization, etc., and learn to apply the tools and practices discussed in the classroom sessions. By the end of the week, participants are expected to have built a cohesive dataset and gained skills in working with data for social science research.
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Origin : Files produced by the author(s)
Licence : CC BY - Attribution
Licence : CC BY - Attribution
Licence : CC BY - Attribution
Origin : Files produced by the author(s)
Licence : CC BY - Attribution

Dates and versions

hal-03923285 , version 1 (04-01-2023)

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  • HAL Id : hal-03923285 , version 1

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Alexandre Chevallier, Jérémy Richard, Geneviève Michaud, Baptiste Rouxel. Python for data science. Doctoral. Summer School 2017, Trivedi Centre for Political Data (TCPD) Ashoka University, India. 2017. ⟨hal-03923285⟩
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