Webpages tagged with «Data Science»
On Thursday the 3rd of August, students will present their findings after having worked for six weeks with cases from three different stakeholders. You are welcome to join the audience!
We would like to invite to the final student presentations in the new summer course “Political Data Science Hackathon” (ISSSV1337).
Ingrid Kristine Glad (dScience)
dScience – Centre for Computational and Data Science – at the University of Oslo (UiO) is an interdisciplinary centre developing and supporting research within computational science and data science across UiO and together with partners in industry and public sector.
In addition to hosting research programs and projects, dScience develops mechanisms collecting, managing and sharing high-quality data for academic and business development purposes in Norway and internationally. dScience focuses on basic, long-term research, creates collaboration across disciplines and sectors, offers community services and contributes to the education and supervision of students.
In this seminar we talk about careers in political data-science.
Optical character recognition (OCR) promises to open vast bodies of historical data to scientific inquiry, but OCR can be cumbersome when documents are noisy. The past 18 months have seen the launch of new OCR processors with vastly improved accuracy. In this seminar, Thomas Hegghammer will give an overview of the latest tools and present a new R package that offers access to the most powerful of them all, Google Document AI.
autoMrP is an R package for small-area estimation that just released on CRAN: https://cran.r-project.org/package=autoMrP. autoMrP estimates sub-national public opinion from survey data and census information.
Silje S. L. Hermansen presents joint work with Urska Sadl entitled Judicious judging: The effect of political debate on judicial decision making
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Philipp Broniecki presents joint work with Lucas Leeman and Reto Wüest on an R-package for Improved multilevel regression with post-stratification through machine learning (autoMrP)
The digitalization of records of political data, historical and current, has the potential to substantively enrich, and challenge, our understanding of political phenomena. The Political Data Science (PODS) research group brings together scholars interested in the collection and exploitation of these new sources of data.
Who will the Norwegian election this fall? While no answer can be definite until election has passed, the large amount of available data allows probabilistic answers. In the past, media coverage of Norwegian elections and popular party support has focused on single polls or averages of polls at best. This is suboptimal, because each poll contains random noise and systematic bias, which makes it hard to accurately gauge how party support develops over time. Furthermore, opinion polls are snapshots and do not directly address the question of how support is likely to develop in the future. To make better use of the data, Jørgen Bølstad has developed a Bayesian model of latent party support and made the results publicly available at estimite.com. In his presentation, Bølstad will introduce the new model and its outputs, including some results that are not available online. He will also give a brief description of the project and how the webpage is built and updated.
Neil Ketchley presents Violent Contention and Decolonization: Evidence from the 1919 Egyptian Revolution
Thomas Hegghammer presents joint work with Neil Ketchley