Data Analysis in R

OVERVIEW
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.

LEVEL
Advanced Bachelor

COURSE CONTENT

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. 

LEARNING OBJECTIVES
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.
• Handle multivariate data and classify them into categories.
• Have developed their skills in programming.

TEACHING METHODS
Interactive seminar

TYPE OF ASSESSMENT
Programming assignments, final exam

TARGET AUDIENCE
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.

ADDITIONAL ENTRY REQUIREMENTS
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. Nonetheless, we will briefly go over these topics again to refresh the memory. Affinity with programming is an advantage in learning R. You should bring a computer on which R (latest version) and R desktop (latest version) is installed. We will do all the exercises in a normal room where you will exclusively work on your own computer.

FIELD VISIT
Optional extracurricular bicycle tour of “new” Amsterdam.