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

Location and time

In the Canvas online learning environment during the duration of the course. See Timing.

Exam schedules

The course workspace will remain open until the end of the course deadline (see Timing), and all assignments must be completed before the workspace closes.

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

Self-paced online course. The course is completed entirely independently by the end of the course deadline. You will study the material in an online learning environment, which includes study materials and assignments/tests. No personal feedback will be provided on the assignments.

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. Laurea degree students and pathway students enroll in the course through Pakki. Enrollments are processed almost daily, allowing you to start the course flexibly when it suits you. You will receive a completion record for the course approximately two weeks after finishing. During holiday periods, enrollments and completion records are processed less frequently. If the course is not completed by the end date of the implementation, the student can enroll in the next implementation. Incomplete assignments cannot be transferred to the next implementation; all tasks must be redone. For content-related inquiries, contact the responsible teacher. For other inquiries, email mooc@laurea.fi.