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: 01.03.2021 - 07.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Pekka Malo
Teacher in charge (applies in this implementation): Pekka Malo, Lina Siltala-Li
Contact information for the course (valid 08.02.2021-21.12.2112):
Professor: Pekka Malo, D.Sc. (quant. methods), M.Sc. (math)- Email: pekka.malo(at)aalto.fi
- Theory-related questions
Assistant1: Lauri Neuvonen, M.Sc. (Engineering Physics and Mathematics)
- Email: lauri.neuvonen(at)aalto.fi
- Questions about assignment and tutorial in module 1
- Use the subject DSBquery in your email
Assistant2:Taeyoung Kee , M.Sc. (ISM)
- Email: teayoung.kee(at)aalto.fi
- Questions about assignment and tutorial in module 4
- Please write to me in English
- Use the subject DSBquery in your email
Assistant3: Lina Siltala-Li, M.Sc. (tech)
- Email: lina.siltala-li(at)aalto.fi
- Questions about assignment and tutorial in module 2 & 5
- Questions about enrolment
- Practical arrangements of the course
- 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
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:
See Syllabus
Workload
Valid 01.08.2020-31.07.2022:
Contact teaching 50 h, Independent work 110 h.
Applies in this implementation:
See Syllabus
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
To be defined in the course syllabus.
Applies in this implementation:
See Syllabus
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.
Registration for Courses
Valid 01.08.2020-31.07.2022:
Via WebOodi
Applies in this implementation:
See Syllabus
FURTHER INFORMATION
Further Information
Valid 01.08.2020-31.07.2022:
Limited amount of students will be admitted to the course. The quota is announced separately on a yearly basis. 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 studentsApplies in this implementation:
See Syllabus
- Opettaja: Kee Taeyoung
- Opettaja: Malo Pekka
- Opettaja: Neuvonen Lauri
- Opettaja: Siltala-Li Lina