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, visualisation 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.
Upon completing 3 out of 5 courses in the specialisation (no more than one text mining course), students can obtain a certificate. Each course may also be taken separately.
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.
The course teaches students the necessary skills to understand how R works, and how to use R for a variety of statistical analysis of data in many domains of science. The skills addressed in this practical are:
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Five full days. A typical course day starts at 9.00 and ends at 17.00 with breaks for coffee, lunch and tea.
Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.
Housing through: Utrecht Summer School.
You can choose between two options for participating in this course, but please note that there is always the possibility that we have to change the course pending COVID19-related developments:
If you are interested in the campus option, let us know via a message in the application form under ‘Student Comment’.
The physical course costs €720, but if you participate via the livestream you will get a 100 euro discount. Note that if you choose the campus option, you will be asked to first pay the livestream-fee (€620) and, when we have permission from the university to actually organise classes on location, we will send a second invoice for the remainder of the fee. This way, you will be ensured to have at least a spot for the livestream.
Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
Please include a short description about your (scientific) background, and what you expect to learn from this course (or would like to learn).
Irma Reyersen | E: email@example.com