Data Science: Statistical Programming with R
Faculty of Social and Behavioural Sciences
15 July 2019
19 July 2019
Utrecht, The Netherlands
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.
The course deals with the following topics:
1. An introduction to the R environment.
2. Basic to advanced programming skills: data generation, manipulation, pipelines, summaries and plotting.
3. Fitting statistical models: estimation, prediction and testing.
4. Drawing statistical inference from data.
5. Basic statistical learning techniques.
6. Bootstrapping and Monte Carlo simulation.
The course starts at a very basic level and builds up gradually. At the end of the week, participants will master advanced programming skills with R. No previous experience with R is required.
Participants are requested to bring their own laptop for lab meetings.
This course is part of a series of courses in the Summer School Data Science specialization taught by UU’s department of Methodology & Statistics.
This course can also be taken separately
Summer School Data Science specialization:
1. Data science: Statistical Programming with R (this course) (S24: 15-19 July)
2. Data science: Multiple Imputation in Practice (S28: 22 – 25 July)
3. Data science: Data analysis & visualization (S31: 29 July – 2 August)
Upon completing all courses in the specialization, students can obtain a certificate. Each course may also be taken separately.
Dr. Gerko Vink
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.
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 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:
• 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.
Five full days.
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.
Utrecht Summer School does not offer scholarships for this course.
01 July 2019