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
MADIGE
Digital Business Opportunities, Masters, Pasila
MADIGF
Digitaalisen liiketoiminnan mahdollisuudet, masterit, Pasila
EXCH
EXCH 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)"

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