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Data Science: Statistical Programming with R (Utrecht Summer School)
This course offers an elaborate introduction to statistical programming with 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 a very popular and powerful 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 broad coverage of statistical procedures, including many that are not available in any other statistical program. However, R lacks standard GUI menus from which to choose what statistical test to perform or which graph to create. Consequently, R is more challenging to master. This course will help flatten the learning curve for those who wish to begin working with R by offering an elaborate introduction to statistical programming in R.
In this course we will cover the following topics:
- An introduction to the R environment
- Basic to advanced programming skills: data generation, manipulation, pipelines, summaries, and plotting
- Fitting statistical models: estimation, prediction, and testing
- Drawing statistical inference from data
- Basic statistical learning techniques
- Bootstrapping and Monte Carlo simulation
The course starts at a very basic level and builds up gradually. So, no previous experience with R is required. At the end of the week, participants will master advanced programming skills with R.
Application deadline: 20 June 2022
This course is part of a series of 5 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics.