Course home page
Welcome to the course!
The course focuses on bridging
the gap between predictive and prescriptive modelling. This will entail
combining probabilistic modelling approaches with modern optimization
techniques and decision-analytic tools. In terms of content, the course
consists of two parts. In the first part, the students will learn
methods and theory (e.g., optimization concepts and fundamentals of
statistical learning theory) needed for prescriptive modelling. The
material will involve programming assignments with practical
applications. The second part will feature applications in the form of
visiting lectures, who come different industries (e.g., financial
analytics, sports analytics).
After completing the course, students will
- understand the importance of prescriptive analytics in business decision-making
- be able to combine predictive modeling approaches with optimization techniques to build prescriptive analytics solutions, and
- be able to implement (program) their solutions with suitable software.
Assessment and grading
Course assessment is comprised of the following two parts:
- Team case (course project): 70%
- Class activity (tutorials, lectures, exercises): 30%.
All assignments must be completed to pass the course. Late assignments will not be accepted. Points from class activities and cases will be scaled to the weights above (i.e. full points from class activity -> 30 course grade precentage). Note that the starting level of the student teams will be taken into account in grading, and thus special attention is paid to the teams’ development in knowledge sharing and learning.
Indicative course grade limits as percentages from full points:
>= 85% -> 5
>= 75% -> 4
>= 65% -> 3
>= 55% -> 2
>= 35% -> 1
These limits may be adjusted if deemed necessary by course staff. To give an example: getting half of class activity points (+15% for the course) and 80% of case points (+56% for course) will result in a total of 71% for the course, implying a grade of 3.
Please take a look at the course syllabus to find important information regarding course content, structure, assessment, etc.
All study materials except the course books will be provided as needed and made available on the pages found in the sidebar.
Contact
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 assignments
- Questions about enrolment
- Practical arrangements of the course
- Use the subject DSBquery in your email
Assistant2: Philipp Back, M.Sc. (econ)- Email: philipp.back(at)aalto.fi
- Questions about assignments and other questions
- Please write to me in English
- Use the subject DSBquery in your email
This course is supported by DataCamp, a great learning platform where you can learn Python, R and SQL through a combination of short expert videos and hands-on-the-keyboard exercises.