Study Guide

Learning outcomes

Student is able to
- Explain why organizations need to apply data in decision making
- Explain where the data comes from and how it can be used (buying, selling, collecting data, big data from IoT, digital platforms and APIs, machine learning and AI vs. statistical methods)
- Identify the role of information in a value creation process
- Identify key business models enabled by digital data
- List a few “no-code” ways of experimenting with data products to validate the business model
- Summarize how intellectual privacy regulations shape the data economy

Teaching methods

Online course available for independent study at the student's own pace

Learning materials and recommended literature

All necessary materials are provided on the course platform.

Alternative completion methods of implementation

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Co-operation with working life and/or RDI

Course has been produced in corporation with Osaango Oy. Lecturers on the videos are experts in data economy.

Important dates

The course has to be completed in Canvas until end of the course end date (31.12.2024).

During the summer holiday in July and during the New Year holidays, no credits are registered. Credits made by the end of the semester are recorded for the past semester, those made by 31.12. for the autumn semester, and those made by 31.7. for the spring semester.

Forms of internationality

The course is offered to partner institutions and its content is designed to be suitable for both EU countries and internationally.

Students workload

1 ECTS credits = 27 hours of work

Content and scheduling

Videos, quizzes and articles in four different modules.
Module 1: Data economy in brief
Module 2: Data sources and business models
Module 3: Role of information in value creation
Module 4: Next steps in data economy

After this course, you will be able to:
Explain why organizations need to apply data in decision making
Explain where the data comes from and how it can be used (buying, selling, collecting data, big data from IoT, digital platforms and APIs, machine learning and AI vs. statistical methods)
Identify the role of information in a value creation process
Identify key business models enabled by digital data.
List a few “no-code” ways of experimenting with data products to validate the business model
Summarize how privacy regulations shape the data economy

Further information for students

The course is master's level and therefore bachelor's level degree student can not enroll for the course.

Inquiries for the course can be directed to mooc@laurea.fi.

Grading scale

Approved/Failed

Evaluation methods and criteria

For a passing grade, student has to complete all the required tests with a minimum score of 50%, all the required assignments and familiarise themselves with the course materials.