Max amount of FITech students: 200
Persons without a valid study right to a Finnish university have preference to this course.
In this course, you will learn the fundamental skills for data collection and preprocessing, enabling you to succeed in your data mining tasks.
Course contents
Amount and quality of data plays a major role in modern machine learning applications. In this course the students are familiarised with different concepts concerning data such as: data gathering, data preprocessing and normalisation, combining data from multiple sources, missing data and outliers.
Learning outcomes
After completing this course student can
- recognise data types and perform required pre-processing steps before further analysis
- design and implement a data collection process
- combine data from different sources
- normalise and transform data, and handle missing or incorrect values
- ensure generalisability of the results.
Course material
Lecture material, including topical videos, exercise materials and quizzes in Moodle. R and Matlab (available in remote desktop).
Teaching methods
The course is conducted through self-paced online learning during the course period. All assignments has to be finished before the course ends.
Completion methods
The course is completed by passing the learning assignments. You need to notify the teacher when you are ready to receive credits from your attainments.
More information in the University of Oulu study guide.
You can get a digital badge after completing this course.