Data Analysis in R

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Data Analysis in R

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study

    Computer Programming

  • Language Level

    Taught In English

    Hours & Credits

  • ECTS Credits

    3
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    4
  • Overview

    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. 

    We 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: modeling relationships among the data and predicting future observations. After that we will extend the linear model to the generalized linear framework, in order to analyse non-normally distributed variables. In the second week we focus on a common problem in statistics: classification. We explore the two main areas of classification (supervised learning and unsupervised learning) with theory and examples.

    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 various popular R packages, 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 examination

    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.