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 basic principles of predictive modeling and gain experience in using data analytic tools that are widely used in companies.

Credits: 6

Schedule: 07.01.2019 - 19.02.2019

Teacher in charge (valid 01.08.2020-31.07.2022): Pekka Malo

Teacher in charge (applies in this implementation): Philipp Back, Johanna Bragge, Pekka Malo

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


  • Valid 01.08.2020-31.07.2022:

    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 01.08.2020-31.07.2022:

    Course project 40%, Assignments 30%, Exam 30%.

  • Valid 01.08.2020-31.07.2022:

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


Study Material
  • Valid 01.08.2020-31.07.2022:

    To be defined in the course syllabus.

  • Valid 01.08.2020-31.07.2022:

    Prior knowledge in programming is recommended (e.g., Basics in Programming; 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.