Data-driven Decision Making (5 cr)

Learning outcomes

The student is able to
- use planning, analysis and decision-making tools and techniques in strategic planning
- analyze financial statements and business reports and use them as a basis for decisions
- make business decisions in various business contexts
- apply data in decision making

Location and time

Please refer to the Timetable Engine at https://lukkarit.laurea.fi/ for the schedule information. Laurea reserves the right to modify the timetable.

Materials

Strategic Analytics. Harvard Business Review Press, 2020.

Bernard, M. (2017). Data Strategy

Jackson, P. & Carruthers, C. (2019). Data Driven Business Transformation. John Wiley & Sons, Incorporated.

+ Materials in course Canvas.

Teaching methods

The study unit is conducted online and does not require attendance on campus.

The study unit includes scheduled teaching or guidance online via the ZOOM platform, and these sessions will not be recorded. The learning environment is Canvas, where you can find the schedule, materials, assignments, and other information for the course.

The study unit includes working individually and in pairs or groups. The study unit requires active participation and commitment to interactive studying. You will receive individual feedback for assignments from the teacher and peer feedback may also be utilised.

Employer connections

There will be cooperation with companies that use data in their business planning and scoring.
Regarding tools, partner companies are:
- SAS Analytics - students will achieve data related SAS Data Literacy Essentials and SAS Data Litteracy in Practice Badges
- Microsoft - students will become familiar with data tools PowerBI and Excel

Exam schedules

Based on the degree regulations (2024), the student accepted for the implementation is required to confirm their participation by showing activity at the start of the study in the following way:

In order to demonstrate activity, the student must be present at the first contact lesson or notify the teacher in charge of their absence to confirm his/her participation in the study. Registration will be rejected if the student does not report his/her absence at the start of the study or the reason for the absence cannot be considered justified. Another student can be taken in his place. The first contact lesson is 26.8.2025.

Canvas opens latest on 26.8.2025.

International connections

The study unit is suitable for exchange students.

Completion alternatives

According to the degree regulations (2024), "Students are entitled to apply for recognition of prior learning regardless of where, when and how the competence has been acquired. ... At Laurea, there are two different procedures for the recognition and accreditation of prior learning: a) accreditation of prior learning (competence acquired in higher education studies at another institution) and b) demonstration of competence (competence acquired in other ways). The recognition and accreditation of prior learning is initiated by the student themselves. The student is responsible for demonstrating and verifying their competence. The student is entitled to guidance for the recognition and accreditation of their competence." Further information in the student intranet.

Student workload

Workload of the study is measured in a way that to acquire the required competence of the study unit, one credit corresponds to an average of 26.7 hours of work done by the student. The actual time needed varies e.g. according to prior competence. 5 ECTS is approximately 135 hours.

Content scheduling

Module 4: Data and Artificial Intelligence in Business Context

The weight of the modules in assessment:
Mod. 1: 20% (Individual)
Mod. 2: 20% (Individual)
Mod. 3: 40 % (In pairs)
Mod 4: Project 20% (Team)
All modules are compulsory.

Please note that the course will be in English on the ZOOM platform.

Evaluation scale

H-5

Further information

The study unit corresponds to the requirements of Bachelor's level education.