Syllabus

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: 24.10.2023 - 29.11.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Pekka Malo, Iaroslav Kriuchkov

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

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

• valid for whole curriculum period:

  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.

Assessment Methods and Criteria

• valid for whole curriculum period:

  To be defined in the course syllabus.

Workload

• valid for whole curriculum period:

  To be defined in the course syllabus.

DETAILS

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  To be defined in course syllabus.

  Teaching Language : English

  Teaching Period : 2022-2023 II
  2023-2024 II

  Enrollment :

  The selection is made by Sisu automatically based on the priority groups.

  The priority for the student selection is as follows:

  1. Aalto ISM MSc students.

  2. Students in Master’s Programme in ICT Innovation (EIT digital).

  3. Bachelor’s students in Business with 150 credits complete.

  4. Other Aalto students.

Description