Basic information
Code: SCI3066
Extent: 20 - 25 credits
Language: English
Teacher in charge: Jari Saramäki
Administrative contact: Minna Parviainen
Target group: Master students with sufficient prerequisite knowledge
Application procedure: Open for all students of Aalto University
Quotas and restrictions: No quotas
Prerequisites: Elementary university-level mathematics: calculus, linear algebra, probability and
statistics. Programming (knowledge of Matlab and/or Python will help).
Objectives
TThe aim is to introduce the student to the computational and theoretical background that is necessary for a quantitative understanding of complex systems, from the human brain to a diversity of biological and social systems. The skills learned here are helpful for students considering interdisciplinary scientific careers, or, e.g. for industrial data scientist positions.
Content and structure of the minor
Code | Name | Credits |
Compulsory courses | 10 | |
Statistical Inference | 5 | |
Complex Networks | 5 | |
Elective courses | 10 - 15 | |
Select as many courses as needed to fulfill the 20 - 25 credit requirement | ||
| CS-E5795 | Computational Methods in Stochastics | 5 |
| MS-C2111 | Stochastic Processes | 5 |
| CS-E5745 | Mathematical Methods for Network Science | 5 |
| MS-E2112 | Multivariate Statistical Analysis | 5 |
| CS-E5755 | Nonlinear Dynamics and Chaos | 5 |
| CS-E5700 | Hands-On Network Analysis | 5 |
| CS-E4710 | Machine Learning, Supervised methods | 5 |
| CS-E5710 | Bayesian Data Analysis | 5 |
Information Visualization | 5 | |