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Data Analysis in R
Interdisciplinary Studies Program
Amsterdam, Netherlands
Dates: early Jul 2025 - early Aug 2025
Data Analysis in R
OVERVIEW
CEA CAPA Partner Institution: Vrije Universiteit Amsterdam
Location: Amsterdam, Netherlands
Primary Subject Area: Computer Sciences
Instruction in: English
Transcript Source: Partner Institution
Course Details: Level 300
Recommended Semester Credits: 3
Contact Hours: 45
Prerequisites: A completed undergraduate course in statistics and an acquaintance with basic linear algebra, the fundamentals of hypothesis testing, linear regression analysis and statistical tests such as the t-test.
DESCRIPTION
With the increasing use of alternative software packages like R in data analysis, now is the time to learn their ins and outs. The large number of active programmers creating R packages makes this an up-to-date programme providing a huge range of statistical analyses. This course focuses on understanding statistical models and analysing the results whilst learning to work with R. As well as introducing the software to newcomers, it presents basic and more advanced statistics using an overarching framework of the generalized linear model.
The first week is devoted to regression analysis, and learning how to use R (i.e. run analyses, visual representation and test assumptions). We start with descriptive statistics and visual representation of data, which is the first step for most statistical analyses. We then introduce the linear regression model, a widely used model with two main purposes: modelling relationships among the data and predicting future observations.
In the second week, we will extend the linear model to the generalized linear framework, in order to analyse non-normally distributed variables. You will learn how to reduce data dimensions using principal component analysis and how to analyse multi-item scales using confirmatory factor analysis. Multiple item scales are used to validly measure complex constructs, however, in the model you would like to have only one measurement.
Despite the speed with which we highlight major themes in statistics, affinity with programming is an advantage in learning R. You will use a computer on which R (latest version) and R desktop (latest version) is installed.
By the end of this course, students will be able to:
- evaluate the quality of quantitative data sources
- choose the appropriate method for analysis, depending on the data source
- conduct various statistical tests
- analyse data using generalized linear framework
- analyse multi-item scales using principal components and factor analysis
- have developed their skills in R programming
Every day consists of short lectures with examples, and exercises in which you apply what you have learnt right away. The focus in the exercises and assignment is the coding in R and how to apply and to interpret generalized linear regression models. After class, you are supposed to work on an assignment in which you integrate what you've learnt in the exercises during class. This assignment will be graded.
Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course. CEA CAPA's recommended credits are based on the contact hours assigned by Vrije Universiteit Amsterdam (VU Amsterdam): 15 contact hours equals 1 U.S. credit
The first week is devoted to regression analysis, and learning how to use R (i.e. run analyses, visual representation and test assumptions). We start with descriptive statistics and visual representation of data, which is the first step for most statistical analyses. We then introduce the linear regression model, a widely used model with two main purposes: modelling relationships among the data and predicting future observations.
In the second week, we will extend the linear model to the generalized linear framework, in order to analyse non-normally distributed variables. You will learn how to reduce data dimensions using principal component analysis and how to analyse multi-item scales using confirmatory factor analysis. Multiple item scales are used to validly measure complex constructs, however, in the model you would like to have only one measurement.
Despite the speed with which we highlight major themes in statistics, affinity with programming is an advantage in learning R. You will use a computer on which R (latest version) and R desktop (latest version) is installed.
By the end of this course, students will be able to:
- evaluate the quality of quantitative data sources
- choose the appropriate method for analysis, depending on the data source
- conduct various statistical tests
- analyse data using generalized linear framework
- analyse multi-item scales using principal components and factor analysis
- have developed their skills in R programming
Every day consists of short lectures with examples, and exercises in which you apply what you have learnt right away. The focus in the exercises and assignment is the coding in R and how to apply and to interpret generalized linear regression models. After class, you are supposed to work on an assignment in which you integrate what you've learnt in the exercises during class. This assignment will be graded.
Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course. CEA CAPA's recommended credits are based on the contact hours assigned by Vrije Universiteit Amsterdam (VU Amsterdam): 15 contact hours equals 1 U.S. credit