Data Analysis in R

Learn the Ins and Outs
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. Researchers also use R to write functions for analysing data, or to create professional plots.
Course levelAdvanced Bachelor/Master
Session 130 June to 14 July 2018
Recommended course combinationSession 2: Artificial Intelligence For a Healthy LifeWildlife Crime Analysis: Data-Driven Nature Protection
Session 3: Big Data Management and Analysis in Linux, Operations Research: A Mathematical Way to Optimize Your World 
Co-ordinating lecturersAndrea Bassi
Other lecturersDr. Meike Morren
Form(s) of tuitionInteractive seminar
Form(s) of assessmentProgramming assignments, final examination
ECTS3 credits
Contact hours45
Tuition fee€1000
Students or professionals in the field of Economics, Social Sciences or any other field with an interest in quantitative data analysis using R. No programming experience is required. PhD students with a deficit in statistics or wishing to refresh their knowledge are also welcome. If you have doubts about your eligibility for the course, please let us know. Our courses are multi-disciplinary and therefore are open to participants with a wide variety of backgrounds.
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 using an overarching framework of generalized linear modelling. We start with descriptive statistics, before moving on to basic tests and simple regression. You also learn how to analyse the multi-item scales which are often applied in survey research using exploratory and confirmatory factor analysis. We then introduce the generalized linear framework to analyse non-normally distributed variables and, lastly, multi-level modelling.

Throughout the course you will work with R, conducting exercises that teach you how to analyse multi-item scales, how to analyse relationships among binary and interval variables and how to apply generalized linear regression models.

By the end of the two weeks you are acquainted with various popular R packages, can write your own functions and can use attractive plots to present your data.

At the end of this course you can:

  • Evaluate the quality of quantitative data sources.
  • Choose the appropriate method for an analysis, depending upon the data source.
  • Conduct various statistical tests.
  • Analyse data using generalized linear framework.
  • Decide when to use latent variable modelling.
  • Enjoy your developed programming skills.
Optional extracurricular bicycle tour of “new” Amsterdam, rounded off with a drink.


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 VU University Medical Center, on the BIOMARKER project. The goal of this project is to design a Bayesian adaptive clinical trial to decide on the optimal targeted treatment strategy for patients with diffuse large B-cell lymphoma (DLBCL). Furthermore, Andrea collaborates with the VU University as a teaching assistant in the area of biostatistics, for bachelor and master programs. His main research interests are Bayesian statistics, statistical programming and decision theory. 

"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."

Readings to be provided at the start of the course.
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.
Bernard"I think this is an important course to take because in the 21st century Data is power. You see companies such as google and Facebook that do a lot of data collection and to make use of that data you have to analyze it, so this is a good course to introduce those techniques. It's interesting because the Dutch have historically been aware of the importance of analyzing data. During the Golden Era, one of the reasons the Dutch East India company had such strong cartographers (map makers), and what they did was they had these cartographers travel and explore. Collecting data for future traders so that they could know the best spots to trade and that's how they used data during that time to advance their business. The importance of data has only grown today, whether you're running a technological company or a marketing company and therefore I found the course very practical." -Bernard Wong
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