LEARNING OUTCOMES
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: 06.09.2022 - 18.10.2022
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Pekka Malo, Antti Suominen, Philipp Back
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:
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 for whole curriculum period:
To be defined in course syllabus (pdf).
Workload
valid for whole curriculum period:
To be defined in course syllabus (pdf).
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 I
2023-2024 IEnrollment :
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.