Data Science: Statistical Programming with R
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 do 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.
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.
After registration we will ask you to briefly describe your statistical programming experience (none required) as well as your expectations from this course.
A maximum of 80 participants will be allowed in this course.
The skills addressed in this practical are:
• working with the R environment.
• using R-functions for data generation, manipulation and summaries.
• making high-quality plots.
• Forming pipelines
• Reproducible programming
• Statistical inference
• Basic statistical learning
• Fitting and interpreting a variety of statistical models.
Programming of bootstraps and Monte Carlo simulations.
Housing through Utrecht Summer School
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.
Write a short description about your (scientific) background, and what you do expect to learn from this course (or would like to learn)
For more information click "LINK TO ORIGINAL" below.