Data Analysis in R VU Graduate Winter School - Vrije Universiteit Amsterdam

Learn the ins and outs of Data Analysis in R!

Students should apply for Data analysis in R to discover the enormous potential of the open-source programming language R and to develop a series of skills and tools to analyse statistical problems of a diverse nature.

Almost every major organizations and universities use R. Google not only uses R but has also written standards for the language that are widely accepted.

This course focuses upon 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.

We will 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. After that, we will extend the linear model to the generalised linear framework, in order to analyse non-normally distributed variables.

Each day consists of short lectures with examples and exercises in which you apply what you have learned right away. At the end of the course there will be a written assignment which will be graded.

Continue reading below for additional course information.

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Andrea Bassi holds a MSc in Engineering Mathematics (Polytechnic University of Milan), with a focus on Applied Statistics. After having worked in Italy as a statistical consultant, he started his PhD training in Biostatistics at the Vrije Universiteit Medical Center, on the BIOMARKER project.

At the moment he works as Data Scientist at Hypermynds, where he crunches R on a daily basis for many different applications!

"Students should apply for Data analysis in R to discover the enormous potential of the open-source programming language R and for acquiring a series of skills and tools to analyze statistical problems of diverse nature."

Learning objectives

By the end of the course, students will be acquainted with various popular R packages, and will be able to perform different statistical analyses, writing their own functions and use attractive plots to present their data.
Forms of tuition and assessment

The mode of assessment will be an assignment in written form which will be handed in at the end of the course. The assignment consists of two or three practical exercises to be solved using R and commenting the programming outputs in a short essay format. Both statistical and coding parts will be graded.
Each day will be split up into three sections: typically theory - exercise – theory with short breaks of 15/30 minutes in between.
Entry requirements

Even though students from different backgrounds are welcome, at least an undergraduate course in statistics is required to guarantee the necessary knowledge to follow the course.
Course schedule

The course will last for five days between 8-12 January and takes place from 9 AM to 1 PM (CET). During the afternoons, some exercise sessions will be scheduled (self-work with live Q&A in a virtual classroom environment).
The course will be taught through online lectures and exercise classes but students will be expected to dedicate an additional 45 hours (approx.) to self-study.
The course syllabus will be published closer to the date of the course.
Course syllabus

Here you can download the preliminary course syllabus.
^{*Note that it may still be subject to some changes}