Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


After passing the course the students can conduct simple multivariate statistical analyses. They are familiar with common multivariate data analysis techniques. Students are familiar with different multivariate location and scatter functionals and the corresponding estimates and they understand the basic properties of these functionals. Students know how to apply principal component analysis and how to robustify the method. Students can conduct bivariate and multiple correspondence analysis and interpret the findings. Students are familiar with canonical correlation analysis and they can apply the method in practice. Students know several approaches to discriminant analysis and classification including different depth based methods. They are also able to assess the goodness of the classification. Students are familiar with hierarchical clustering methods and moving centers -type clustering methods and they understand the restrictions of these approaches. Moreover, students are not only able to apply multivariate methods in practice, but they also understand the mathematics and the reasoning behind the methods.


Credits: 5

Schedule: 08.01.2024 - 19.04.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Pauliina Ilmonen, Jaakko Pere

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English


  • valid for whole curriculum period:

    The course is an introduction to multivariate statistical analysis. Course topics include multivariate location and scatter, principal component analysis (PCA), robustness and robust PCA, bivariate correspondence analysis, multiple correspondence analysis (MCA), canonical correlation analysis, discriminant analysis, statistical depth functions, classification and clustering. Software R is used in the exercises of this course.


Assessment Methods and Criteria
  • valid for whole curriculum period:

    Homework assignments, exercise points, exam, compulsory project work.


  • valid for whole curriculum period:

    Lectures 24h (2), Exercises 24h (2), Project work 40h, Homework assignments 30 h, reading and studying the lecture materials 20 h



Study Material
  • valid for whole curriculum period:

    Lecture slides and lecture notes. Students are expected to either attend the lectures or ask for lecture notes from their fellow students.


Substitutes for Courses


Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring III - IV
    2023-2024 Spring III - IV

    Enrollment :

    Registration for Courses: In Sisu (