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Data analytics and artificial intelligence (4cr)

Code

General information

Timing: 12.01.2026 - 30.04.2026; The implementation has not yet started.

Number of ECTS credits allocated: 4 cr

Local portion: 4 cr

Mode of delivery: Face-to-face

Unit: School of Technology

Teaching languages: Finnish

Degree programmes: Bachelor's Degree Programme in Information and Communications Technology

Teachers: Antti Häkkinen

Groups: ZJA26KTIDA1
Avoin amk, tekn, IT, Osaamiskokonaisuus, Data-analytiikka 1

Course: TT00CD84

Unfortunately, no reservations were found for the realization Data analytics and artificial intelligence TT00CD84-3007. It's possible that the reservations have not yet been published or that the realization is intended to be completed independently.

Evaluation scale

0-5

Objective

In the course, you will get an overview of the methods, possibilities and applications of data analytics and artificial intelligence, as well as the most common programming environments and libraries used in them.

EUR-ACE Knowledge and understanding
You understand the key concepts of data analytics and artificial intelligence. In addition, you will recognize the different stages of data processing and the libraries suitable for them.

EUR-ACE Engineering practice
You know how to choose libraries suitable for different stages of data analytics and machine learning and use them as part of data processing.

Content

This course will give you a comprehensive overview of the methods, possibilities and applications of data analytics and artificial intelligence. You will learn to understand the key concepts and identify the different stages of data processing and the libraries that can be used for them. You will be able to select and use libraries suitable for the different stages of data analytics and machine learning in practical data processing. This course will enable you to apply data analytics and artificial intelligence methods in a variety of situations.

Data sources
Data analysis
Data visualisation
Machine learning

Location and time

Online implementation

Materials

Course material page (lecture materials, exercises)

Teaching methods

- Online implementation, where the student can proceed at their own pace and return exercises at any point before the given final return date.

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

Distance learning 108 h (environment preparation, familiarisation with the material, exercises)

Assessment criteria, satisfactory (1)

Sufficient (1):
You know the basic concepts of data analytics and artificial intelligence superficially. You know how to use some methods as part of data processing.

Satisfactory (2):
You know the key concepts of data analytics and artificial intelligence. You know how to apply your knowledge at a basic level in data processing problems.

Assessment criteria, good (3)

Good (3):
You understand the key concepts and basic principles of data analytics and artificial intelligence. You know how to apply your knowledge consistently and successfully in most data processing situations.

Very good (4):
You have a deep understanding of the key concepts and methods of data analytics and artificial intelligence. You know how to apply your information effectively and analyze the results even in more demanding data processing problems.

Assessment criteria, excellent (5)

Excellent (5):
You have an excellent command of the concepts of data analytics and artificial intelligence and combine information from different sources seamlessly. You can apply your knowledge effectively and analyze the results clearly and critically even in more demanding data processing problems.

Qualifications

Basics of Programming

Further information

The course assessment consists of returned exercises (no separate exam). There is one given deadline for returning exercises for the course, before which the exercises must be submitted for assessment. The student may submit exercises for assessment at any point in the course.