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Data Analysis in R

Data is everywhere but to retrieve the valuable insights requires important analytical skills. The large number of active programmers creating R packages makes R suitable for a range of data analysis techniques, from basic hypothesis testing to generalized linear regression, and multivariate analysis such as principal component, factor analysis, or clustering. You will apply what you have learned right away in short exercises using Rmarkdown. You will be graded using an assignment in which you will learn to deal with messy data and integrate the knowledge you obtained in the exercises. The course is highly intensive as it focuses both on interpreting statistics while also learning to program in R.

Course description

With the increasing use of programming languages in data analytics, now is the time to learn their ins and outs. This course focuses upon understanding statistical models and analyzing 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.

The first week is devoted to learning how to use R and regression analysis. We start with reading data into R, descriptive statistics and visual representation of data, which is the first step for statistical analyses. We then introduce the linear regression model, a widely used model with two main purposes: modeling relationships among the variables and predicting future observations.

In the second week, we will extend the linear model to the generalized linear framework, in order to analyze discrete dependent variables. The logit regression that you will work with, proves useful to understand the remainder of the course: classification. You will learn how to reduce data dimensions using principal component analysis and  cluster analysis, and how to use the learned methods for prediction.

Every day consists of short lectures with examples, and exercises in which you apply what you have learned right away. The focus in the exercises and assignment is the coding in R and how to apply and to interpret generalized linear regression models. After class, you are supposed to work on an assignment in which you integrate what you have learned in the exercises during class. This assignment will be graded.

Download here the detailed preliminary course syllabus.

Continue reading below for more information

About this course

Course level

  • Master / Advanced / PhD

Course coordinator

  • Dr. Meike Morren

Credits

  • 3 ECTS

Contact hours

  • 45

Language

  • English

Tuition fee

  • €735 - €1310

Additional course information

  • Learning objectives

    By the end of this course, students will be able to:

    • evaluate the quality of quantitative data sources
    • choose the appropriate method for analysis, depending on the data source
    • conduct various statistical tests
    • analyse data using generalized linear framework
    • have developed their skills in programming
  • Form of tuition and assessment

    Every day consists of short lectures with examples, and exercises in which you apply what you have learnt right away. The focus in the exercises and assignment is the coding in R and how to apply and to interpret generalized linear regression models. After class, you are supposed to work on an assignment in which you integrate what you’ve learnt in the exercises during class. This assignment will be graded.

    • The course will consist of lectures, exercises and self-study. 
    • The assignment: analysis, with interpretation handed in using a knitted Rmarkdown document
  • Preliminary course syllabus

    Here you can download the preliminary course syllabus for the summer 2024 course. 

    *Please note that it is a preliminary syllabus and that it still might be subject to change.  

  • Additional course requirements

    Successfully completed a statistics course on Bachelor level (hypothesis testing, regression analysis).

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  • Celia
  • Summer and Winter School Officer
Celia VU Amsterdam Summer & Winter School
  • Esther
  • Summer and Winter School Officer

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