Syllabus

Title
0493 Advanced Data Analysis with R
Instructors
Dr. Marcus Wurzer
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/16/24 to 10/01/24
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Monday 10/07/24 11:45 AM - 01:15 PM D2.0.031 Workstation-Raum
Monday 10/14/24 11:45 AM - 01:45 PM D2.0.031 Workstation-Raum
Monday 10/21/24 11:45 AM - 01:45 PM D2.0.031 Workstation-Raum
Monday 11/04/24 11:45 AM - 01:45 PM D2.0.031 Workstation-Raum
Monday 11/11/24 12:15 PM - 01:45 PM D2.0.031 Workstation-Raum
Monday 11/18/24 12:15 PM - 01:45 PM D2.0.031 Workstation-Raum
Monday 11/25/24 12:15 PM - 01:45 PM D2.0.031 Workstation-Raum
Monday 12/02/24 12:15 PM - 01:45 PM D2.0.031 Workstation-Raum
Monday 12/09/24 12:15 PM - 01:45 PM D2.0.031 Workstation-Raum
Monday 12/16/24 12:15 PM - 01:45 PM D2.0.031 Workstation-Raum
Monday 01/13/25 11:45 AM - 01:45 PM D2.0.031 Workstation-Raum
Monday 01/20/25 11:45 AM - 01:45 PM D2.0.031 Workstation-Raum
Monday 01/27/25 11:45 AM - 01:45 PM D2.0.031 Workstation-Raum
Contents

R is a high-level language and an environment for data analysis and data visualization. While many important statistical methods are already included in the base R installation, 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 standard part that focuses on the following:

  • An introduction to R
  • Dynamic documents with R Markdown and Quarto
  • Linear Models: Simple and Multiple Linear Regression, ANOVA/ANCOVA, descriptive statistics and visualization, diagnostics, data transformations, model selection procedures, model plots (effect displays and posterior predictive checks), design matrices/contrasts
  • Generalized Linear Models: Binary, Multinomial and Proportional-Odds Logistic Regression, Poisson and Negative-Binomial Regression, odds ratios, maximum likelihood estimation, descriptive statistics and visualization, diagnostics etc. (as specified for the linear models above)

Depending upon students' interests and the data sets they want to analyze, a selection of these additional methods may be covered:

  • Mixed-Effects Models
  • Decision Trees
  • Classification methods: Naive Bayes, k-NN, ...
  • Cluster Analysis: Hierarchical, non-hierarchical, parametric/model-based
  • Correspondence Analysis
  • Principal Components Analysis
  • Multidimensional Scaling
  • Social Network Analysis
  • ...
Learning outcomes

Upon 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
  • Attendance is compulsory. Students have to attend classes for at least 80% of the total time, i.e., 18 of 22.5 hours. If you know you will miss a class, please inform me in advance!
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 %)
Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Recommended previous knowledge and skills

Introductory statistics

Availability of lecturer(s)
marcus.wurzer@wu.ac.at
Other

Please note that this is a BYOD course, i.e., since WU doesn't provide computers, students will have to concern themselves with a working installation of R on their laptops that is required for participation in class.

Last edited: 2024-08-04



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