Credits: 6

Schedule: 07.01.2020 - 18.02.2020

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)

Assistant1Philipp Back, M.Sc. (econ)
  • Email: philipp.back(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): 

III Spring (2018-2019) Otaniemi campus

III Spring (2019-2020) Otaniemi campus

Learning Outcomes (valid 01.08.2018-31.07.2020): 

After completing the course, students will understand the basic principles of predictive modeling and gain experience in using data analytic tools that are widely used in companies.

Content (valid 01.08.2018-31.07.2020): 

Fundamental concepts in predictive analytics, classification and association mining, model evaluation, use of programming (e.g., python or R), visiting lectures, project work.

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

See Syllabus (pdf)

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Course project 40%, Class activity 30%, Exam 30%.

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

See Syllabus (pdf)

Workload (valid 01.08.2018-31.07.2020): 

Contact teaching 50 h, Independent work 107 h, Exam 3 h.

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

See Syllabus (pdf)

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)

Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

Prior knowledge in programming is required, at least Programming I (37C00400) or equivalent knowledge. Working knowledge of statistics and linear algebra is also required. Programming II (37C00450) and Data Resources Management (37E01600) are highly recommended as prior courses.

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): 

Maximum number of students accepted is 50.

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 & Students in Master's Programme in in ICT Innovation (EIT digital)
3. Other Aalto MSc students

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

See Syllabus (pdf)

Details on the schedule (applies in this implementation): 

See Syllabus (pdf)


Registration and further information