Data Analytics with Python (5 cr)

Evaluation scale

H-5

Schedule

Detailed schedule will obey the duration and the contents of the course. It will be published in the beginning of implementation in Moodle if the programme director decides to start this implementation.

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

Study material is delivered through Moodle. There is also a lot of online mataerial availeble.

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 are also encouraged to study independently with the help of ready-made examples, within the rules of Haaga-Helia.

No previous coding experience is required.

Working life connections

The course content takes into account and builds on the needs of today's working life.

Exam dates and re-exam possibilities

Detailed examination arrangements are agreed together within the implementation participants. Participants may use online material and modern coding environment in the examination, due to which a classroom examination, during which a participant's identity is checked, is necessary. Particularly, no Exam examination is possible.

All the examinations of methodological studies will be held under supervision at the premises of the Haaga-Helia UAS and the identity of the participants will be checked during the examination.

Internationality

Analytical skills are international skills. Data analytics skills are international skills. Mathematical and methodological skills are international skills

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
Description and analysis od time series
Time series forecasting
Predictive analytics and basics of 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.