Business Analytics (5 cr)
Code: DIG4HM102-3002
Basic information of implementation
- Enrollment
- 15.06.2020 - 28.08.2020
- Enrolment for the implementation has ended.
- Timing
- 28.08.2020 - 18.12.2020
- Implementation has ended.
- ECTS Credits
- 5 cr
- Campus
- Pasila Campus
- Teaching languages
- English
- Seats
- 15 - 40
- Degree programmes
- LEBUM Degree Programme in Leading Business Transformation
- LUJOM Degree Programme in Business Development and Leadership
- PAKEM Degree Programme in Service Business Leadership and Development
- LITEM Degree Programme in Business Technologies
- Teachers
- Lili Aunimo
- Jouni Soitinaho
- Groups
-
MADIGEDigital Business Opportunities, Masters, Pasila
-
MADIGFDigitaalisen liiketoiminnan mahdollisuudet, masterit, Pasila
-
EXCHEXCH Exchange students
- Course
- DIG4HM102
Evaluation scale
H-5
Implementation methods, demonstration and Work&Study
Depending on the implementation, learning takes place in contact lessons, independent studies, teamwork and online-studies. The course includes the assessment of one’s own learning.
The course has a project work, which is centered around a business case to which several data analytics and machine learning methods are applied. The topic may represent a real case occurring in a company or it may be picked up from a set provided by the course organiser. In the case study, the student will learn both how to create value for business as well as how to implement advanced business analytics methods. This casework is done in student groups that consist of both technically and business-wise skilled students.
In addition to the project work, the following learning methods are used: flipped classroom method, in-class and individual assignments, teamwork in contact lessons, and online lessons.
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)"