17 July 2020
on course website
Data Analysis in R (online course)
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.
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 the generalized linear model. We start with descriptive statistics and simple regression, before moving on to multiple regression.
Many problems in data analysis are related to dimension reduction, from data mining problems such as classification to analyzing survey answers. You will learn how to reduce data dimensions using principal component analysis and how to analyse multi-item scales using confirmatory factor analysis. Additionally, you learn how to treat missing data in various models.
Lastly we will introduce how to create and adjust plots in R. Every day consists of short lectures with examples, and exercises in which you apply what you have learned right away. Each week you are supposed to make an assignment which is graded. The focus in the exercises and assignment is the coding in R and how to apply and to interpret generalized linear regression models. By the end of the two weeks you are acquainted with numerous basic functions available in R can write your own functions and can use attractive plots to present your data.
Meike Morren, Andrea Bassi
Students or professionals with an interest in quantitative data analysis using R. We will use examples from Economics, Social Sciences and Biostatistics. No programming experience is required. PhD students 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.
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.
Contact Hours: 45
Do you want to make the most out of your summer? You can combine this course with a course in session 2 to create a 4-week Online Summer School.
EUR 500: The tuition fee for a two-week online course is €500. This tuition fee includes:
•Two week online course
•Exclusive content for a limited number of students
•Transcript of records with a maximum of 3 ECTS (European study credits)
•Certificate of attendance after completing the course
•Individual/group guidance from professor
•Full support from the summer school team
•Optional (virtual) social programme
on course website