Introduction to Data Economy (1 cr)

Objectives (course unit)

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

Exam schedules

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.

Assessment 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.

Assessment scale

Approved/Failed

Teaching methods

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

Learning materials

All necessary materials are provided on the course platform.

Student workload

1 ECTS credits = 27 hours of work

Content 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

Completion alternatives

-

Practical training and working life cooperation

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

International connections

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

Further information

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.