https://puma.ub.uni-stuttgart.de/tag/Python,PUMA RSS feed for /tag/Python,2026-04-16T08:33:56+02:00https://puma.ub.uni-stuttgart.de/bibtex/2e996e5d39e64311a76b19be06077f5d6/rapheiraphei2020-05-04T15:23:37+02:00Python, R, computational data development management, practices, research science, social theory <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Raphael H. Heiberger" itemprop="url" href="/person/1dc27cef78a9b9c7791531e9dcd6f85c6/author/0"><span itemprop="name">R. Heiberger</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jan R. Riebling" itemprop="url" href="/person/1dc27cef78a9b9c7791531e9dcd6f85c6/author/1"><span itemprop="name">J. Riebling</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Methodological Innovations</span>, </em> </span>(<em><span>Januar 2016<meta content="Januar 2016" itemprop="datePublished"/></span></em>)</span>Mon May 04 15:23:37 CEST 2020Methodological Innovationsjan1--11Installing computational social science: {Facing} the challenges of new information and communication technologies in social science92016Python, R, computational data development management, practices, research science, social theory Today’s world allows people to connect over larger distances and in shorter intervals than ever before, widely monitored by massive online data sources. Ongoing worldwide computerization has led to completely new opportunities for social scientists to conceive human interactions and relations in unknown precision and quantities. However, the large data sets require techniques that are more likely to be found in computer and natural sciences than in the established fields of social relations. In order to facilitate the participation of social scientists in an emerging interdisciplinary research branch of “computational social science,” we propose in this article the usage of the Python programming language. First, we carve out its capacity to handle “Big Data” in suitable formats. Second, we introduce programming libraries to analyze large networks and big text corpora, conduct simulations, and compare their performance to their counterparts in the R environment. Furthermore, we highlight practical tools implemented in Python for operational tasks like preparing presentations. Finally, we discuss how the process of writing code may help to exemplify theoretical concepts and could lead to empirical applications that gain a better understanding of the social processes initiated by the truly global connections of the Internet era.