Professor in charge: Professor Jari Saramäki
Extent: 60 credits
Abbreviation: CS
Code: SCI3060
The aim is to give the students a strong computational and theoretical background for understanding complex systems, from the human brain to a diversity of biological and social systems. The major has been structured such that the student can choose which areas to emphasize (e.g. neuroscience, theory of complex systems, machine learning). After completing their studies, the students have the necessary skills for interdisciplinary scientific careers, or, e.g. for data scientist positions in the industry.
The major has been structured to allow for flexibility, and the student may emphasize chosen areas of interest. In addition to courses common to all Life Science Technologies masters, the major has four modules: 1) Measuring and interpreting data, 2) Advanced statistics and machine learning, 3) Systems and Modeling, and 4) Application areas. The student must pick at least one course from each module; the student is free to choose the rest of the courses freely from all modules.
Descriptions of the modules:
1. Measuring and interpreting data: courses for dealing with experimental data, its analysis, and visualization.
2. Advanced statistics and machine learning: Bayesian statistics and methods, basic principles of machine learning.
3. Network and systems: network science, chaos theory, nonequilibrium statistical mechanics, mathematical modeling.
Application areas: courses in all other Life Science Technologies majors.
Code 
Course 
Credits 
Period/Year 
Compulsory common courses of the programme (15 credits): 

Experimental and Statistical Methods in Biological Sciences 
5 
III/1 

Life Science Technologies Project Course 
10 
IIIV/1 

Compulsory courses of the major (45 credits). Pick at least one course from each four modules: 

1 Measuring and intepreting data 

Information Visualization 
5 
IV/1 

Handson Network Analysis 
5 
IVV/1 

Multivariate statistical analysis 
5 
IIIIV/1 

2 Advanced statistics and machine learning 

Bayesian Data Analysis 
5 
III/1 

Work Course on Bayesian Analysis 
2 
IIIV/1 

Machine Learning: Basic Principles 
5 
I/2 

Algorithmic Methods of Data Mining 
5 
I/1 or 2 

Deep Learning 
5 
II/2 

Special Course in Machine Learning and Data Science 
110 
year 2 

3 Networks and systems 

Complex Networks 
5 
III/1 

Mathematical Methods for Network Science 
5 
III/1 or 2 

Modeling Biological Networks 
57 
III/1 

Computational Science  5  III/1  
Nonlinear Dynamics and Chaos  5  IIIIV/I or 2  
Special Assignment in Complex Systems 
510 
year 2 

Special Course in Complex Systems 
36 
1 or 2 



4 Application areas 

Pick any course from the other Life Science Technologies majors. 
In their elective studies, the students are encouraged to take courses from other majors of the LifeTech programme, according to their interests. Courses in the field of information and computer science are also recommended. Also internship is recommended in elective studies.