You can find course descriptions in Sisu. In your study plan, choose the course and click the course code or search courses by code or name. Learning environments are found in MyCourses through search or after registration in "My own courses".
Complex Systems (minor)
Basic information
Code:
SCI3066
Extent:
20–25
ECTS
Curriculum:
2024–2026
2022–2024
2020–2022
Previous curricula
Level:
Advanced studies
Language of learning:
English
Theme:
Health and wellbeing
ICT and digitalisation
Target group:
All Aalto students
Teacher in charge:
Jari Saramäki
Administrative contact:
Päivi Koivunen
Organising department:
Department of Computer Science
School:
School of Science
Prerequisites:
Elementary university-level mathematics: calculus, linear algebra, probability and statistics. Programming (knowledge of Matlab and/or Python will help).
Quotas and restrictions:
No quotas
Application process:
Open for all students of Aalto University
Content and structure of the minor
About the minor
After completing the studies in this minor the student has a basic understanding of complex systems. Further, students will be able to apply computational and theoretical tools specific to the field of complex systems to analyze and solve problems. Upon completion, the students have developed skills towards interdisciplinary scientific careers, or, e.g. for data scientist positions in the industry.
Content
| Code | Course name | ECTS | Period |
|---|---|---|---|
| CS-E5775 | Complex Systems | 5 | I |
| CS-E5740 | Complex Networks | 5 | I-II |
Select courses to fulfil the 20-25 ECTS requirement |
|||
| CS-E5795 | Computational Methods in Stochastics | 5 | I-II |
| MS-C2111 | Stochastic Processes | 5 | II |
| CS-E5745 | Mathematical Methods for Network Science | 5 | III |
| MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV |
| CS-E5755 | Nonlinear Dynamics and Chaos | 5 | III-IV |
| CS-E5700 | Hands-on Network Analysis | 5 | IV-V |
| CS-E5885 | Modeling Biological Networks | 5 | II |
| CS-E4730 | Computational Social Science | 5 | IV-V |
| CS-E4715 | Supervised Machine Learning | 5 | I-II |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| CS-E4840 | Information Visualization | 5 | IV |
| MS-C1620 | Statistical Inference | 5 | III-IV |
Previous curricula
Basic information
Code: SCI3066
Extent: 20–25 ECTS
Language of instruction: English
Level: Masters
Theme: Health and wellbeing ICT and digitalisation
Curriculum: 2022–2024
Target group: All Aalto students
Teacher in charge: Jari Saramäki, Mikko Kivelä (1.8.2023-31.7.2024)
Administrative contact: Päivi Koivunen
Organising department: Department of Computer Science
Prerequisites: Elementary university-level mathematics: calculus, linear algebra, probability and statistics. Programming (knowledge of Matlab and/or Python will help).
Quotas and restrictions: No quotas
Application process: Open for all students of Aalto University
Structure and content of the minor
Objectives
The 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.
Structure of the minor
| Code | Course name | ECTS 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-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-E5885 | Modeling Biological Networks | 5 |
| CS-E4730 | Computational Social Science | 5 |
| CS-E4710 | Machine Learning: Supervised Methods | 5 |
| CS-E5710 | Bayesian Data Analysis | 5 |
| CS-E4840 | Information Visualization | 5 |
Basic information
Code: SCI3066
Extent: 20 - 25 credits
Language: English
Teacher in charge: Jari Saramäki
Administrative contact: Päivi Koivunen
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 | Course name | ECTS 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-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 |
| CS-E4840 | Information Visualization | 5 |
Basic information
Code: SCI3066
Extent: 20 - 25 credits
Language: English
Teacher in charge: Jari Saramäki
Administrative contact: Päivi Koivunen
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
The 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 | Course name | ECTS credits |
|---|---|---|
| Compulsory courses | 10 | |
| MS-E2115 | Experimental and Statistical Methods in Biological Sciences |
5 |
| CS-E5740 | 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-E3210 | Machine Learning: Basic Principles | 5 |
| CS-E5710 | Bayesian Data Analysis | 5 |
| CS-E4840 | Information Visualization | 5 |
Code: SCI3066
Extent: 20 - 25 credits
Language: English
Teacher in charge: Jari Saramäki
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).
Content and structure of the minor
The 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.
Structure of the minor
| Code | Course name | ECTS credits |
|---|---|---|
| Compulsory courses | 10 | |
| MS-E2115 | Experimental and Statistical Methods in Biological Sciences |
5 |
| CS-E5740 | Complex Networks | 5 |
| Elective courses | 10 - 15 | |
| Select as many courses as needed to fulfill the 20 - 25 credit requirement | ||
| CS-E4840 | Information Visualization | 5 |
| CS-E5700 | Hands-On Network Analysis |
5 |
| CS-E5720 | Work Course on Bayesian Analysis | 2 |
| CS-E4070 | Special Course in Machine Learning and Data Science | 5 |
| CS-E3210 | Machine Learning: Basic Principles | 5 |
| CS-E5745 | Mathematical Methods for Complex Networks | 5 |
| CS-E5880 | Modelling Biological Networks | 5-7 |
| CS-E5755* | Nonlinear Dynamics and Chaos* |
5* |
| CS-E5790 | Computational Science | 5 |
*The course is not lectured in 2017 - 2018