The goal of this course is to provide an accessible entry into the world of R which prepares the participants to confidently approach the most common analysis tasks using R. This course is particularly recommended for those who want to take a methods course follow-up that relies on R.
Qualitative Comparative Analysis models causal complexity by analyzing necessary or sufficient conditions for an outcome. This course introduces both the nuts and bolts of QCA, and the most advanced analytic tools available in the R software environment needed for a publishable QCA.
This course aims to sharpen R skills and provide materials for R users who are keen to learn R beyond the basics. We will cover how to write and work efficiently, build better functions, create R packages and use version control (git).
The goal of this course is to provide an accessible entry into the world of R, preparing participants to approach the most common analysis tasks using R, including data cleaning, exploratory data analysis and creating engaging visualizations.
The course covers multilevel regression analysis with R for two types of data; individuals nested within social contexts (e.g. cross-national surveys), and repeated observations from individuals (e.g. longitudinal panel studies).
The increasing availability of large amounts of online data enables new types of research in the social sciences. This course equips participants with the R programming skills necessary to gather online data and process them into formats they can use in their research.
Whether you are a social scientist, a business analysist or a data journalist, analysing data is key to greater understanding of the world around us. Whether it’s understanding political discussions online, the diffusion of news events, or the predictive power of certain indicators, systematic analysis improves business conduct, news analysis and reporting and understanding of human behaviour in general.
Due to the further digitization of society in general, digital data have become available in large quantities (Big Data). Because Big Data (many observations) is also often high-dimensionally (many features, variables), it allows us to use machine learning techniques to make predictions and classify data into groups and uncover hidden patterns in data.