Data extraction


The course will introduce four core themes

  • data collection using computational means
  • computing descriptive results from the data and using them in data analysis
  • data mining using supervised methods (SVMs, association rules)
  • data mining using unsupervised methods (k-means, topic models)

Course practices

Course is organised in two parts: first half we introduce to specific methods described in the objectives and in later part, students will produce an independent research project applying these methods.

Course prerequisites

Taking part of this course will require good knowledge on Python language and familiarity with qualitative and quantitative research methods. There is a set of prep questions available on the course page to check your understanding of the prerequisites.


This course is graded as pass/fail. Passing grade requires a report written from the final report. The structure and content of the final report will be specified later on.

Enroll via WebOodi, more details in the course page and University of Helsinki Moodle.


Matti Nelimarkka (

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