Credits: 6

Schedule: 25.02.2019 - 11.04.2019

Teacher in charge (valid 01.08.2018-31.07.2020): 

Pekka Malo

Teaching Period (valid 01.08.2018-31.07.2020): 

IV Spring (2018-2019) Otaniemi campus

IV Spring (2019-2020) Otaniemi campus

Learning Outcomes (valid 01.08.2018-31.07.2020): 

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.

Content (valid 01.08.2018-31.07.2020): 

Predictive models (e.g,.regression and time series models), prescriptive optimization models (e.g, linear and convex), R programming, visiting lectures, project work.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Course project 50%, Assignments and class activity 50%, a more detailed description on assessment criteria is given in the syllabus

Workload (valid 01.08.2018-31.07.2020): 

Contact teaching 50 h, Independent work 110 h.

Study Material (valid 01.08.2018-31.07.2020): 

To be defined in the course syllabus.

Prerequisites (valid 01.08.2018-31.07.2020): 

Data Science for Business I (30E03000) and Business Decisions 1 (27C01000) or 2 (30E02000); or equivalent skills. Intermediate / advanced skills in R programming. Time series analysis (30E00800) and Simulation (30E00400) are recommended.

Grading Scale (valid 01.08.2018-31.07.2020): 


Registration for Courses (valid 01.08.2018-31.07.2020): 

Via WebOodi

Further Information (valid 01.08.2018-31.07.2020): 

A maximum of 50 students will be admitted to the course. Students are prioritized in the following order:
1. Aalto ISM MSc students whose specialization area is Business Analytics
2. Aalto Analytics and Data Science minor students
3. Other Aalto MSc students


Registration and further information