10 July 2021
on course website
Data Science: Statistical Programming with R
Due to the covid-19 outbreak, this course has been postponed to 2021.
R is rapidly becoming the standard platform for data analysis. This course offers an elaborate introduction into statistical programming in R. Students learn to operate R, form pipelines for data analysis, make high quality graphics, fit, assess and interpret a variety of statistical models and do advanced statistical programming. The statistical theory in this course covers t-testing, regression models for linear, dichotomous, ordinal and multivariate data, statistical inference, statistical learning, bootstrapping and Monte Carlo simulation techniques.
R is rapidly becoming the standard platform for data manipulation, visualization and analysis and has a number of advantages over other statistical software packages. A wide community of users contribute to R, resulting in an enormous coverage of statistical procedures, including many that are not available in any other statistical program. Furthermore, it is highly flexible for programming and scripting purposes, for example when manipulating data or creating professional plots. However, R lacks standard GUI menus, as in SPSS for example, from which to choose what statistical test to perform or which graph to create. As a consequence, R is more challenging to master. Therefore, this course offers an elaborate introduction into statistical programming in R. Students learn to operate R, make plots, fit, assess and interpret a variety of basic statistical models and conduct advanced statistical programming and data manipulation. The topics in this course include regression models for linear, dichotomous, ordinal and multivariate data, statistical inference, statistical learning, bootstrapping and Monte Carlo simulation techniques.
Dr. Gerko Vink
Applied researchers and (master) students who already use statistical software and would like to learn to use, or improve their usage of the flexible R-environment. Understanding of basic statistical theory such as t-tests, hypothesis testing and regression is required. Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication sciences, will benefit from the course. A maximum of 80 participants will be allowed in this course. Please note that the selection for this course will be done on a first-come-first-served basis.
EUR 700: Course + course materials + lunch
EUR 200: Housing fee (optional)
on course website