Curriculum
Overall, the structure of the Master’s Degree was designed based on the strategy of the Foundation and the Department of Business Administration, the active participation of postgraduate students, the experience of external bodies and graduates of other Master Programmes from the labor market, the planned volume of studies according to the (ECTS) system for level 7, the possibility of providing work experience opportunities to students, the connection of teaching with research and the relevant institutional framework and the official process of approval of the Master’s Degree by the Foundation.
The total number of Credit Units (ECTS) required to obtain the Master’s Degree is seventy-five (75). The duration of studies for the award of the Postgraduate Diploma is defined as one full calendar year, i.e. two (2) academic semesters and the summer period in accordance with applicable legislation. Successful completion of all courses offered, as distributed over the two semesters (A and B), as well as the writing of a Postgraduate Diploma Thesis is required. The language of instruction for the courses and the writing of the Postgraduate Diploma Thesis is defined as Greek and/or English.
The way the educational process is organized is twenty-five percent face-to-face (25%), while fifty percent (50%) of the courses are conducted remotely in a synchronous format and twenty-five (25%) in an asynchronous format.
The start of the courses is set for the last week of October and the end of the courses upon completion of the 13 weeks of teaching per semester.
The structure, content, organization of the courses and teaching methods are oriented towards deepening knowledge in the field of Applied Bioinformatics and Biological Data Analysis and the acquisition of the corresponding skills for their application, through practical skills development courses (hands on, workshops), research methodology courses, with participation in the analysis of data originating from research projects and MDEs with an exclusively research character. The course program is structured per semester as follows:
Fundamental knowledge
The course outline is available here.
Concepts related to UNIX programming and the shell, R and R-studio (basic commands, data frames, functions and charts), Python (data and variable types, functions and modules) are explained and examples of programming, statistical analyses with R and Python and troubleshooting in the use of these tools are presented. In addition, introductory tutorials with basic concepts of molecular biology and genetics are offered for students without a biological background. The aim of the course is for students to understand the fundamental knowledge of bioinformatics and biological data processing, so that they can expand into the demanding material of the rest of the MSc program.
Principles of Bioinformatics
The course outline is available here.
It includes concepts related to Bioinformatics and Computational Biology, Machine Learning, Neural Networks, Deep Learning, Transcriptomics, Genomics, Epigenetics, Algorithms and their applications in the Biological Sciences. The aim of the course is to provide the theory behind the production and utilization of biological data, the theory and available tools of Bioinformatics and Computational Biology, in order to perform comprehensive analysis of biological data with modern techniques.
Principles of Data Analysis
The course outline is available here.
The basic concepts of Systems Biology, Structural Biology, Genomics and Metagenomics are explained and tools for NGS data analysis, biomolecule structure analysis, population genetic analyses, information organization and metagenomics applications are presented. The aim of the course is to provide the theory and tools required for biological data analysis and to gain experience in their use, building on the theoretical basis in Bioinformatics acquired in the first semester to perform comprehensive analysis.
Bioinformatics and Data Analysis Applications
The course outline is available here.
The course includes a series of hands-on exercises where practical training is provided in basic biological data analysis techniques, such as the analysis of the three-dimensional structure of macromolecules, the analysis of unknown genomes, immunoinformatics, single cell RNA-seq analysis, metabarcoding analysis of the microbiome in environmental samples, NGS applications in clinical diagnosis, machine learning applications in “Omics” and the application of Federated Machine Learning models for protected data in the health sector. The aim of the course is for students to become familiar with these analyses.