Data Analytics with Python (5 cr)

Evaluation scale

H-5

Schedule

Period 2, Autumn 2024

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

https://1u.fi/hhjuhanurmonen (available via vdi)

Teaching methods and instruction

In the classroom or online classes, one will learn the basics of data analytics using Python coding under the guidance of a teacher. Students can also study independently with the help of ready-made examples.

No previous coding experience is required.

Completion alternatives

The implementation allows one to participate in common teaching according to your own skills. The course material supports indivudual learning.

Learning assignments

The implementation includes individual tasks where the skills learned are applied. The topics of the tasks include

Descriptive analytics
Diagnostic analytics
Time series description and analysis
Time series forecasting
Predictive analytics and machine learning models

Assessment methods

Individual assignments are assessed and each contributes its own weight to the final grade. Each individual assignment must be passed according to the criteria given in the implementation during the same course implementation.