Treffer: Python for data science

Title:
Python for data science
Contributors:
Centre de données socio-politiques de Sciences Po (Sciences Po, CNRS) (CDSP), Sciences Po (Sciences Po)-Centre National de la Recherche Scientifique (CNRS), Fondation Nationale des Sciences Politiques FNSP, Institut des Sciences Humaines et Sociales - CNRS Sciences humaines et sociales (INSHS-CNRS), Centre National de la Recherche Scientifique (CNRS), LIA SPINPER (France-Inde), Trivedi Centre for Political Data (TCPD) Ashoka University
Source:
https://sciencespo.hal.science/hal-03923285 ; Doctoral. Summer School 2017, Trivedi Centre for Political Data (TCPD) Ashoka University, India. 2017 ; Summer School 2017.
Publisher Information:
CCSD
Publication Year:
2017
Subject Geographic:
Time:
Trivedi Centre for Political Data (TCPD) Ashoka University, India
Document Type:
lecture
Language:
English
Rights:
http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.BC623DFF
Database:
BASE

Weitere Informationen

1. Introduction (Python, Jupyter Notebooks)2. Data scraping3. Data visualization ; Doctoral ; COURSE OBJECTIVEPolitical 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.