LEARNING OUTCOMES
The objective of the course is to provide an introduction to practical data science from the perspective of business
analysts. Part of the course material touches also more advanced topics to provide further content for the students that are interested in knowing more about the underlying theory and techical aspects.
After completing the course, students will understand the basic principles of predictive modeling and gain experience in using machine learning models to solve business problems.
Credits: 6
Schedule: 04.09.2024 - 10.10.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Pekka Malo
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:
Topics covered include basics of predictive modeling, classification, variable selection, hyperparameter tuning, ensemble learning, evaluation of models, problem of overfitting and its avoidance.
The course has a strong focus on empirical assignments, which require prior knowledge in statistics and basic skills in programming/scripting (or at least willingness to learn).
Assessment Methods and Criteria
valid for whole curriculum period:
Assignments, quizzes, course project
Workload
valid for whole curriculum period:
Lectures, tutorials, assignments, course project
DETAILS
Study Material
valid for whole curriculum period:
Lectures (recordings, slides), tutorials (recordings, notebooks), other material defined in syllabus.
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: 2024-2025 Autumn I
2024-2025 Spring IV
2025-2026 Autumn I
2025-2026 Spring IVRegistration:
The selection is made by Sisu automatically based on the priority groups. The priority for the student selection is as follows 1. Students in Master's Programme in Information or Service Management and Master's Programme in Business Analytics.* 2. Students in other Master’s Programmes in Aalto BIZ 3. Bachelor’s students in Business with 150 credits complete. 4. Other Aalto students. (*) If you are a Bachelor's student in business with 150 credits about to enter Master's Programme in Information and Service Management or Master's Programme in Business Analytics, please contact the course staff before the end of the registration period to discuss eligibility for the first priority group. Please note that late registrations are not accepted.