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, students will understand the fundamental difference between predictive and prescriptive analytics, and be able to build prescriptive models to support business decision making.
Schedule: 25.02.2019 - 11.04.2019
Teacher in charge (valid 01.08.2020-31.07.2022): Pekka Malo
Teacher in charge (applies in this implementation): Pekka Malo, Lauri Neuvonen
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
Prescriptive optimization models (e.g., linear and convex), time to event anal-ysis, natural language processing, introduction to deep learning, and visiting lectures. The content may vary on a yearly basis depending on the lecturers. A more detailed description of content is provided in syllabus.
Assessment Methods and Criteria
Weekly assignment in Python (No
exam). A more detailed description
on assessment criteria is given in
Course project 50%, Assignments and class activity 50%, a more detailed description on assessment criteria is given in the syllabus
Contact teaching 50 h, Independent work 110 h.
To be defined in the course syllabus.
Data Science for Business I (30E03000) and Business Decisions 1 (27C01000) or 2 (30E02000); or equivalent skills. Intermediate / advanced skills in Python or R programming. Courses dealing with simulation methods and time series analysis are recommended.