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): | |||
Experimental and Statistical Methods in Biological Sciences | 5 | I-II/1 | |
Life Science Technologies Project Course | 10 | III-V/1 | |
Compulsory courses of the major (pick at least 25 credits) | |||
| CS-E5740 | Complex Networks (recommended) | 5 | I-II/1 |
| CS-E5795 | Computational Methods in Stochastics | 5 | I-II/1 |
| MS-C2111 | Stochastic Processes | 5 | II/1 |
| CS-E5745 | Mathematical Methods for Network Science | 5 | III/1 |
Multivariate statistical analysis | 5 | III-IV/1 | |
| CS-E5755 | Nonlinear Dynamics and Chaos | 5 | III-IV/1 |
| CS-E5700 | Hands-on Network Analysis | 5 | IV-V/1 |
Elective courses of the major (pick enough courses for 60 credits in total) | |||
| Theme I: Network and systems | |||
| CS-E5885 | Modeling Biological Networks | 5 | III/1 |
| MS-E1603 | Random Graphs and Network Statistics | 5 | I/2 |
| MS-E2122 | Nonlinear Optimization | 5 | II/1 or 2 |
| MS-E1602 | Large Random Systems | 5 | IV/1 |
| MS-E1050 | Graph Theory | 5 | I/1 or 2 |
| CS-E4555 | Combinatorics | 5 | V/1 |
| CS-E5780 | Special Assignment in Complex Systems | 5-10 | I-V (on request) |
| CS-E5770 | Special Course in Complex Systems | 1-10 | I-V/1 |
| Theme II: Data science and machine learning | |||
| CS-E4840 | Information Visualization | 5 | IV/1 |
Machine Learning: Basic Principles | 5 | I/2 | |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II/1 |
Algorithmic Methods of Data Mining | 5 | I/1 or 2 | |
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.