Credits: 6

Schedule: 24.02.2020 - 01.04.2020

Teacher in charge (valid 01.08.2018-31.07.2020): 

Pekka Malo

Contact information for the course (applies in this implementation): 

Professor: Pekka Malo, Ph.D. (quant. methods), M.Sc. (math)

Professor: Eeva Vilkkumaa, D.Sc. (tech)

Assistant1Lauri Neuvonen, M.Sc. (tech)
  • Email: lauri.neuvonen(at)
    • Questions about assignments, tutorials, and grading
    • Please write to me in English
    • Use the subject DSBquery in your email
Assistant2Lina Siltala-Li
  • Email: lina.siltala-li(at)
    • Questions about enrolment
    • Practical arrangements of the course
    • Scheduling of presentations
    • Please write to me in English
    • Use the subject DSBquery in your email

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.

Details on the course content (applies in this implementation): 

See syllabus (pdf) in Materials.

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

Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 

Please note that the above information on assessment criteria is outdated. According to the updated syllabus course project is 70% and assignments and class activity 30%.

See syllabus (pdf) in Materials.

Workload (valid 01.08.2018-31.07.2020): 

Contact teaching 50 h, Independent work 110 h.

Details on calculating the workload (applies in this implementation): 

See syllabus (pdf) in Materials.

Study Material (valid 01.08.2018-31.07.2020): 

To be defined in the course syllabus.

Details on the course materials (applies in this implementation): 

See syllabus (pdf) in Materials.

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

Details on the schedule (applies in this implementation): 

See syllabus (pdf) in Materials.


Registration and further information