Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
After completing the course, student is able to:
- Understand the basic principles and applications of contemporary analytics methods
- Identify business problems that can be addressed using available data
- Formulate analytics problems based on a given business problem
- Select and apply a suitable analytics method for a given analytics problem
- Define the data requirements for a given analytics method
- Compare the benefits and drawbacks of alternative models
- Evaluate the business benefits of a given analytics solution
Schedule: 01.03.2021 - 08.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Jukka Luoma, Lauri Saarinen
Teacher in charge (applies in this implementation): Jukka Luoma, Lauri Saarinen
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
The course provides an overview of the data analytics toolbox including topics such as causal inference, predictive analytics, optimization, and simulation. These topics are approached from two perspectives: 1) how can the analytics toolbox be applied to problems arising in operations management, strategic management, and organizational design and development; 2) what are the implications of increasing reliance on analytics for these fields. The lectures discuss how analytics relates to real-world problems while the weekly assignments provide an opportunity to gain hands-on experience in applying analytics methods. In the group project, the students work in teams to develop an analytics solution for a case organization.
Assessment Methods and Criteria
- Individual assignments
- Group project
- Class activity
- Lectures (24 hours)
- Weekly readings (20 hours)
- Weekly assignments (71 hours)
- Group project (20 hours)
A collection of technical readings and cases assigned by the instructors.
Prerequisites for the course: basic course in statistics (e.g., MS-A0502/ MS-A0509), introductory course in programming is recommended (e.g., CS-A1110).