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 214 July to 28 July 2018
Recommended course combination
Session 1: Programming in Python
Session 3: Big Data Management and Analysis in Linux
Co-ordinating lecturersDr Meike Morren
Other lecturersAndrea Bassi
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 students 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.
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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