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Data analysis and knowledge discovery

Individual course

Course contents

The course introduces methods and algorithms for extracting information and knowledge from datasets. This includes techniques for visualisation, classification, regression, outlier detection, rule induction, model complexity selection, and model validation.

Learning outcomes

This course enables students to learn when and how to apply state of the art data analysis and knowledge discovery tools for data. Students will learn modern data analysis methods and algorithms to discover patterns and trends in large, complex and high-dimensional data sets, and turn data into information and knowledge.


Lectures on campus.

More info on University of Turku’s study guide.

You can get a digital badge after completing this course.

data-analytiikka, visualisointi, luokittelu, regressio, poikkeamat

Responsible teacher

University of Turku
Antti Airola

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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ICT studies,
Technical studies
Data analytics,
Industrial engineering and management
Course code:
Study credits:
0 €
Course level:
Teaching period:
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Application deadline:
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Host university:
University of Turku
Who can apply:
Adult learner,
Degree student
Teaching method:
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General prerequisites:
Python programming skills. Basic knowledge of probability, statistics and linear algebra is beneficial.
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