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.

LEARNING OUTCOMES

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.

Credits: 6

Schedule: 24.02.2020 - 01.04.2020

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

Teacher in charge (applies in this implementation): Pekka Malo

Contact information for the course (valid 07.02.2020-21.12.2112):

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)aalto.fi
    • 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)aalto.fi
    • Questions about enrolment
    • Practical arrangements of the course
    • Scheduling of presentations
    • Please write to me in English
    • Use the subject DSBquery in your email


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

CONTENT, ASSESSMENT AND WORKLOAD

Content
  • Valid 01.08.2020-31.07.2022:

    Prescriptive optimization models (e.g., linear and convex), time to event anal-ysis, natural language processing, introduction to deep learning, and visiting lectures. The content may vary on a yearly basis depending on the lecturers. A more detailed description of content is provided in syllabus.

  • Applies in this implementation:

    See syllabus (pdf) in Materials.

Assessment Methods and Criteria
  • Valid 01.08.2020-31.07.2022:

    Spring 2021:
    Weekly assignment in Python (No
    exam). A more detailed description
    on assessment criteria is given in
    the syllabus.

    Spring 2022:

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

     

     

  • 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 01.08.2020-31.07.2022:

    Contact teaching 50 h, Independent work 110 h.

  • Applies in this implementation:

    See syllabus (pdf) in Materials.

DETAILS

Study Material
  • Valid 01.08.2020-31.07.2022:

    To be defined in the course syllabus.

  • Applies in this implementation:

    See syllabus (pdf) in Materials.

Prerequisites
  • Valid 01.08.2020-31.07.2022:

    Data Science for Business I (30E03000) and Business Decisions 1 (27C01000) or 2 (30E02000); or equivalent skills. Intermediate / advanced skills in Python or R programming. Courses dealing with simulation methods and time series analysis are recommended.

FURTHER INFORMATION

Details on the schedule