Professor in charge:  Professor Jari Saramäki

Extent: 60 credits

Abbreviation: CS

Code: SCI3060

Objectives

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 to emphasize the theory of complex systems or data science. Further, it is possible to add courses from other Life Science Technologies majors: e.g. the student can have a degree with 20 cr of data science and networks courses together with 25 cr of neuroscience courses. After completing their studies, the students have the necessary skills for interdisciplinary scientific careers, or, e.g. for data scientist positions in the industry.

Content and structure

The major has been structured to allow flexibility, and the student may emphasize chosen areas of interest. In addition to courses common to all Life Science Technologies masters, the major has a set of seven courses (35 cr) out of which at least five (25 cr) have to be chosen. After this, the student is free to choose the rest from two themes (Networks and Systems, Data Science and Machine Learning) as well as from other Life Science Technologies majors. It is, therefore, entirely possible to build a major that contains the fundamentals of complex systems and a number of neuroscience courses, or a combination of network science and machine learning, or a more mathematical networks track including courses from the department of mathematics. The student can also suggest other topics (economics, social sciences, etc); we are flexible and willing to tailor degrees that match the needs of the student.


NOTE: teaching period of CS-E4555 Combinatorics has changed from III-IV to V. Curriculum has been updated 3.6.2019. 


Code

Course

Credits

Period/Year

Compulsory common courses of the programme (15 credits):

MS-E2115

Experimental and Statistical Methods in Biological Sciences

5

I-II/1

JOIN-E3000

Life Science Technologies Project Course

10

III-V/1

Compulsory courses of the major (pick at least 25 credits)

CS-E5740Complex Networks (recommended)5I-II/1
CS-E5795Computational Methods in Stochastics5I-II/1
MS-C2111Stochastic Processes5II/1
CS-E5745Mathematical Methods for Network Science5III/1

MS-E2112

Multivariate statistical analysis

5

III-IV/1

CS-E5755Nonlinear Dynamics and Chaos5III-IV/1
CS-E5700Hands-on Network Analysis5IV-V/1

Elective courses of the major (pick enough courses for 60 credits in total)

Theme I: Network and systems
CS-E5885Modeling Biological Networks5III/1
MS-E1603Random Graphs and Network Statistics5I/2
MS-E2122Nonlinear Optimization5II/1 or 2
MS-E1602Large Random Systems5IV/1
MS-E1050Graph Theory5I/1 or 2
CS-E4555Combinatorics5V/1
CS-E5780Special Assignment in Complex Systems5-10I-V (on request)
CS-E5770Special Course in Complex Systems1-10I-V/1
Theme II: Data science and machine learning
CS-E4840Information Visualization5IV/1

CS-E3210

Machine Learning: Basic Principles

5

I/2

CS-E5710Bayesian Data Analysis5I-II/1

CS-E4600

Algorithmic Methods of Data Mining

5

I/1 or 2

CS-E4890

Deep Learning

5

IV-V/1

CS-E4640

Big Data Platforms

5

I-II/2

Theme III: pick any courses from other Life Science Technologies majors

Recommendations for elective studies

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.