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 basic principles of predictive modeling and gain experience in using data analytic tools that are widely used in companies.
Credits: 6
Schedule: 11.01.2021 - 23.02.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Pekka Malo
Teacher in charge (applies in this implementation): Philipp Back, Pekka Malo
Contact information for the course (valid 18.12.2020-21.12.2112):
Professor: Pekka Malo, Ph.D. (quant. methods), M.Sc. (math)
- Email: pekka.malo(at)aalto.fi
- Theory-related questions
Assistant1: Philipp Back, M.Sc. (econ)
- Email: philipp.back(at)aalto.fi
- Questions about assignments, tutorials, and grading
- Please write to me in English
- Use the subject DSBquery in your email
Assistant2: Antti Suominen, M.Sc. (tech)
- Email: antti.suominen(at)aalto.fi
- Questions about assignments, tutorials, and grading
- Use the subject DSBquery in your email
Assistant3: Lina 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:
Fundamental concepts in predictive analytics, classification and association mining, model evaluation, use of programming (e.g., python or R), visiting lectures, project work.
Applies in this implementation:
See syllabus (pdf)
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
Course project 40%, Assignments 30%, Exam 30%.
Applies in this implementation:
See syllabus (pdf)
Workload
Valid 01.08.2020-31.07.2022:
Contact teaching 50 h, Independent work 107 h, Exam 3 h.
Applies in this implementation:
See syllabus (pdf)
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
To be defined in the course syllabus.
Applies in this implementation:
See syllabus (pdf)
Prerequisites
Valid 01.08.2020-31.07.2022:
Prior knowledge in programming is recommended (e.g., Basics in Programming; Programming I (37C00400); or equivalent knowledge). Working knowledge of statistics and linear algebra is also required. Programming II (37C00450) and Data Resources Management (37E01600) are highly recommended as prior courses.
Registration for Courses
Valid 01.08.2020-31.07.2022:
Via WebOodi
Applies in this implementation:
See syllabus (pdf)
FURTHER INFORMATION
Further Information
Valid 01.08.2020-31.07.2022:
Course admits only a limited amount of students. Quota is announced separately on 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 & Students in Master's Programme in in ICT Innovation (EIT digital)
3. Other Aalto MSc studentsApplies in this implementation:
See syllabus (pdf)
Details on the schedule
Applies in this implementation:
See syllabus (pdf)
- Teacher: Back Philipp
- Teacher: Malo Pekka
- Teacher: Siltala-Li Lina
- Teacher: Suominen Antti