Data Analytics (5 cr)

Evaluation scale

H-5

Implementation methods, demonstration and Work&Study

Depending on the implementation, learning takes place in contact lessons, as independent studies, teamwork and online-studies. Implementations can include literature, assignments, R&D co-operation and company projects. The course includes the assessment of one’s own learning.

Recognition of prior learning (RPL)
If students have acquired the required competence in previous work tasks, recreational activities or on another course, they can show their competence via a demonstration. The demonstration must be agreed with the course teacher. More information and instructions for recognising and validating prior learning (RPL) are available at https://www.haaga-helia.fi/en/recognition-learning Look at "Instructions to students (master)"

Intro

In modern organizations, information is a crucial tool for management. Data analytics is a means of refining information for business needs. The objective of this course is to understand the process and methods of data analytics and to be able to apply them through practical examples. This course does not require prior programming skills.

Materials

Course material will be available in Moodle.

Teaching methods and instruction

This is a virtual course. Course material (including examples and videos) will be available in Moodle.

Exam dates and re-exam possibilities

No exam.

Learning assignments

There are three exercises. Their topics cover descriptive analytics, diagnostic analytics, time series and machine learning.

Assessment methods

Final grade is the average of assignment grades rounded to integer. All assignments must be passed.