Data-Analysis and Machine Learning Basics (4 cr)

Evaluation scale

0-5

Objective

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used. EUR-ACE Competences: Knowledge and Understanding Engineering Practice

Content

- Structure and implementation of a data-based project - Data analytics and machine learning practices - The concepts of the teaching and test data set and the most common ways of splitting them - Documentation and visualization of the data-based project - Introduction to data analytics and machine learning's most common tools and practical skills needed

Location and time

The course will be implemented in the spring semester of 2025.

Materials

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.

Teaching methods

Virtual study including doing assignments and familiarizing yourself with related lecture and example materials. Assignments are mainly done as group work.

Employer connections

The aim is to connect the content of the course to problems that occur in working life.

Completion alternatives

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.