Credits: 6

Schedule: 07.01.2019 - 19.02.2019

Teacher in charge (valid 01.08.2018-31.07.2020): 

Pekka Malo

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.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

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

Workload (valid 01.08.2018-31.07.2020): 

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

Study Material (valid 01.08.2018-31.07.2020): 

To be defined in the course syllabus.

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


Registration and further information