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

MS-C1620

Statistical Inference

5


CS-E5740

Complex Networks

5

Elective courses

10 - 15

Select as many courses as needed to fulfill the 20 - 25 credit requirement

CS-E5795Computational Methods in Stochastics5
MS-C2111Stochastic Processes5
CS-E5745Mathematical Methods for Network Science5
MS-E2112Multivariate Statistical Analysis5
CS-E5755

Nonlinear Dynamics and Chaos

5
CS-E5700

Hands-On Network Analysis

5
CS-E4710Machine Learning, Supervised methods  5
CS-E5710Bayesian Data Analysis5

CS-E4840

Information Visualization

5