Courses

Web infrastructure, content management systems, strategic communication planning, AI implications, and digital analytics. Students learn to critically evaluate and implement web communication strategies.

Statistical foundations for communication students. Descriptive statistics, probability theory, hypothesis testing, and inferential statistics in R (tidyverse, ggplot2), with examples from media and communications.

Data types and sources, R programming, data visualisation, web scraping, machine learning, text analysis, and reproducible reporting. For graduate sociology and social science students.

Computational approaches to social media data. Platform-specific analysis (Twitter, Facebook, Instagram, LinkedIn), data infrastructure, statistical methods, and open-source analytics tools.

Computational text analysis from data preparation through NLP. Dictionary methods, supervised and unsupervised machine learning, topic modelling, and text clustering in R.

R programming, descriptive statistics, data visualisation, hypothesis testing, categorical analysis, ANOVA, and linear regression for undergraduate social science students.