13 August 2021
on course website
Introduction to Data Science with R and Rstudio for the Social Sciences
Understanding patterns in data is crucial for understanding human societies and making sound decisions. Learn R and Rstudio to become a modern data scientist in the social sciences. Topics that will be discussed in this course are: Reading and cleaning data, descriptive data analysis and visualization, multivariate analysis and scale construction and regression analysis.
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.
To be able to understand quantitative data we need tools of high quality and, if possible, for free. R and R studio are free and multiplatform software applications for descriptive analysis, predictive, and causal analysis, and are increasingly adopted software tools for Data Science in academics, and business (e.g. Microsoft, Google). R also provides numerous tools for publication ready visualization and publication of research results. As such, R and Rstudio are very versatile in handling data.
Topics that will be discussed in the course are:
- Reading and cleaning data
- descriptive data analysis (central tendency measures, correlations) and visualization (barcharts, histograms, scatterplots etc)
- multivariate analysis and scale construction (e.g. factor analysis, correspondence analysis, Mokken analysis)
- regression analysis (choosing the right model, testing for model assumptions)
Maurice Vergeer, Assistant professor,
Communication Science, Radboud University
• Advanced Bachelor
This course is designed for students, early career academics (cf. PhD students), data analysts in companies and data journalists.
After this course you are able to:
1. Distinguish between different types of quantitative data.
2. Choose the correct analytical technique to generate answers to raised questions.
3. Analyse data with and Rstudio with a given technique.
4. Communicate the findings in tabular or visual form.
EUR 0: The fee includes the registration fees, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities.
We offer several reduced fees.Register for this course
on course website