|Study location||Netherlands, Maastricht|
|Type||Summer Courses, full-time|
|Nominal duration||1 week (2 ECTS)|
|Tuition fee||€799 one-time|
Enrolled as an Undergraduate student or Undergraduate diploma
The entry qualification documents are accepted in the following languages: English.
Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.
The language of the course is English, so we expect a fluent level and the ability to follow and participate in class.
In this course, you will be introduced to a wide range of methods and tools utilized by researchers and professionals for analysing and interpreting data using R. R is a powerful open-source programming language and software environment that is increasingly used by analysts for statistical computing and visualizations. You will also learn to use RStudio, an integrated development environment (IDE) that provides a user-friendly interface for working with R. Through this course, you will learn essential skills and knowledge in various data analysis techniques and visualization methods that will enable you to study cross-sectional, longitudinal, and stacked data structures using univariate, bivariate, and multivariate analysis techniques. Additionally, you will learn how to effectively describe data and make meaningful inferences from them. The course will prepare you to conduct independent, data-driven research using R by providing you with ample hands-on experience working individually and in groups on research assignments. The course offers students practical experience in data collection, analysis, and presentation, while also enhancing their programming skills. By the end of the course, you will gain practical experience in data analysis and presentation and have the ability to conduct independent data analysis using R and enhance your skills to address more complex and Big data structures.
• Learn the fundamentals of R and RStudio, including basic programming concepts, packages, and syntax.
• Understand how to use R for statistical computing and graphics.
• Apply essential data analysis techniques and visualization methods using R.
• Study cross-sectional, longitudinal, and stacked data structures using univariate, bivariate, and multivariate analysis techniques.
• Analyse various types of data structures in R, such as time series and networks.
• Enhance statistical literacy and the ability to interpret statistical results.
• Learn best practices for organizing, documenting, and sharing data analysis projects.
• Work collaboratively in teams to conduct data analysis projects.
• Develop a foundation for further study and research in the field of data analysis with R.
• Agresti, A (2018). Statistical Methods for the Social Sciences (Fifth edition). Pearson.
• Horton, N. J., Pruim, R., & Kaplan, D. T. (2015). A Student’s Guide to R. Project MOSAIC
• Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. “ O’Reilly Media, Inc.”.
▪ Lectures ▪ PBL ▪ Work in subgroups
▪ Assignment ▪ Final paper ▪ Oral exam ▪ Presentation
Dr. Eliyahu V. Sapir
Start Date: 31 July 2023
End Date: 4 August 2023