Syllabus

Title
5763 Advanced Data Analysis with R
Instructors
Dr. Marcus Wurzer
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
02/05/19 to 02/20/19
Registration via LPIS
Notes to the course
Subject(s) Doctoral/PhD Programs
Dates
Day Date Time Room
Monday 02/25/19 03:00 PM - 04:30 PM TC.3.02
Monday 03/04/19 01:30 PM - 04:30 PM D2.0.031 Workstation-Raum
Monday 03/11/19 01:30 PM - 04:30 PM D2.0.031 Workstation-Raum
Monday 03/18/19 01:30 PM - 04:30 PM D2.0.031 Workstation-Raum
Monday 03/25/19 01:30 PM - 04:30 PM D2.0.031 Workstation-Raum
Monday 04/01/19 01:30 PM - 04:30 PM D2.0.031 Workstation-Raum
Monday 06/03/19 03:00 PM - 04:30 PM TC.-1.61
Monday 06/17/19 03:00 PM - 04:30 PM LC.-1.038
Monday 06/24/19 01:30 PM - 04:30 PM LC.-1.038

Contents

R is a high-level language and an environment for data analysis and data visualization. While R can be used as a calculator and all important basic statistical methods are included as well, the main benefit is its open-source philosophy which makes R highly extensible and renders possible the availability of new, cutting edge applications in many different fields. The popularity of R increased constantly during the last years and by now, it is arguably the most popular software for data analysis in the statistical community. The course starts with an introduction to R, covers some elementary statistical techniques and then continues with more advanced methods. In particular, the course will focus on the following:

  • Linear Models (Standard Linear Regression and Analysis of Variance)
  • Generalized Linear Models (Binary, Multinomial and Proportional-Odds Logistic Regression, Poisson and Negative-Binomial Regression)
  • Decision Trees
  • Naive Bayes Classifiers
  • k-NN Classifiers
  • Cluster Analysis (hierarchical and non-hierarchical)
  • Correspondence Analysis

Additional methods may be covered as well, depending on student's interest, e.g.,

  • Principal Components Analysis
  • Social Network Analysis
  • Mixed-Effects Models
  • ...

Learning outcomes

On completion of the course students are able to:

  • manipulate and visualize data in R
  • understand the theory and functionality of the methods employed in the course
  • apply the adequate statistical methods to a given problem and perform the statistical calculations using R
  • interpret the results of such analyses
  • communicate and discuss the results of the statistical analysis of data

Attendance requirements

  • Attendence is compulsory. Students have to be attend classes for at least 80% of the total time, i.e., 18 of 22.5 hours.
  • It is not possible to compensate for absences, e.g., if someone misses their project presentation.

Teaching/learning method(s)

Lectures, Practicals

Assessment

  • development of a project concept (10 %)
  • written report on the analysis of a dataset using advanced statistical methods (50 %)
  • oral presentation of analysis results (40 %)

Availability of lecturer(s)

marcus.wurzer@wu.ac.at
Last edited: 2018-10-02



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