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Табови

д-р Анастас Мишев

Табови

Magdalena Kostoska Ph.D.

EuroCC

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eurocc.png

Within the EuroCC project under the European Union’s Horizon 2020 (H2020), participating countries are tasked with establishing a single National Competence Centre (NCC) in the area of high-performance computing (HPC) in their respective countries. These NCCs will coordinate activities in all HPC-related fields at the national level and serve as a contact point for customers from industry, science, (future) HPC experts, and the general public alike. The EuroCC activities—with 33 member and associated countries on board—is coordinated by the High-Performance Computing Center Stuttgart (HLRS). The project aims to elevate the participating countries to a common high level in the fields of HPC, HPDA and artificial intelligence (AI). To this end, the EuroCC project will establish National Competence Centres (NCCs) in the participating countries, which will be responsible for surveying and documenting the core HPC, HPDA, and AI activities and competencies in their respective countries. Ultimately, the goal is to make HPC available to different users from science, industry, public administration, and society.

More information about the project can be found at https://www.eurocc-project.eu/

The role of FCSE, UKIM in the project

FCSE, UKIM is the National HPC Competence Center in Macedonia, taking the leading role in developing the roadmap, capacity building, training as well as the facilitation of access to expertise. It will interface with industry partners to enlarge the HPC application nationwide.

Табови

д-р Марија Михова

Табови

д-р Невена Ацковска

Statistics for data analysis (3+1+1)

1. General Information

This program is designed to train staff with solid statistical knowledge with a focus on the newly recognized field of data science. The curriculum combines rigorous statistical theory with broader practical experience in applying statistical models to data work. Graduates will be in high demand. Most students are expected to be employed as statisticians, analysts and data experts within private and public institutions providing statistical consultations.

  • Name of the proposer: University "Ss. Cyril and Methodius University in Skopje, Faculty of Information Sciences and Computer Engineering - FINKI
  • Title of the study program: Second cycle academic studies in Statistics for Data Analysis
  • Scientific-research area: technical-technological / natural mathematical
  • Field: Informatics / Mathematics
  • Areas: Mathematical Statistics and Operations Research, Data Processing, Applied Mathematics and Mathematical Modeling, Programming, Artificial Intelligence, Algorithms, Information Processing .
  • The value of postgraduate studies is 120 ECTS credits.
  • Duration of studies: 4 semesters .
  • One academic year consists of two semesters lasting 30 weeks (1 semester = 15 weeks).
  • Requirements for enrollment : according to the competition announced by the university, completed undergraduate studies in information science, computer or related fields with a minimum of 180 credits.
  • Introductory Layer : The introductory layer is the first two semesters in which students are offered a set of differential introductory courses. After their successful realization, the student acquires the right to continue with the second year of postgraduate studies.
  • Third semester: 3 Mandatory courses and 2 electives, one of which may be from the University list.
  • Fourth semester : 1 Mandatory course and 1 elective course, the elective course can be from the University list (only if in the first semester the courses are selected at the Faculty level) and the final project - master's thesis from 18 ECTS.
  • 1 ECTS credit corresponds to 30 hours of total work engagement.
  • The number of contact hours is 4.
  • The academic title or degree obtained upon completion of the studies is Master of Science in Information Science - Statistics for Data Analysis

                Master of Science in Informatics - Statistics for Data Analytics

 

2. Introductory layer

The first year of study is the introductory layer for students whose studies lasted less than four years, ie students who gained 180 credits from previous studies. Students must pass differential exams that will enable them to enter the basics of mathematics and computer science needed to successfully complete their studies.

Table 1: List of subjects in the first year of study

РБ CODE / Subject Semester M / E ECTS
1 Mandatory subject 1 from Table 2 VII M 6
2 Mandatory subject 2 from Table 2 VII M 6
3 Mandatory subject 3 from Table 2 VII M 6
4 Mandatory subject 4 from Table 2 VII M 6
5 Elective course 1 * VII E 6
6 Mandatory subject 5 from Table 2 VIII M 6
7 Mandatory subject 6 from Table 2 VIII M 6
8 Mandatory subject 6 from Table 2 VIII M 6
9 Elective course 5 * VIII E 6
10 Selection from the university list of free courses VIII E  

 

Elective courses can be selected from the proposed list of courses of the study program (Table 2), or from the proposed lists of courses from the introductory layer of other study programs of the Faculty of Information Sciences and Computer Engineering. The selection of courses should be made in accordance with the previous knowledge of the candidate and the necessary knowledge to continue with the postgraduate studies in statistics for data analysis. When choosing courses, the student should coordinate with the head of the study program. A free choice of subject is also allowed, which is on the university list of subjects for the first year of two-year postgraduate studies.

After the successful completion of all ten courses and 60 credits, the student with previously acquired 180 ECTS credits (or completed three-year studies) continues with the courses from the second academic year of postgraduate studies - Table 3 (III and IV semester).

  * Selection from the lists of subjects from the introductory layer of all master studies at the Faculty of Information Sciences and Computer Engineering

 

Table 2: List of recommended courses in the first year of study

РБ New code /   Subject Semester ECTS
1 F18L1W011 Discrete Mathematics VII / VIII 6
2 F18L1S013 Calculus VII 6
3 F18L2W006 Probability and statistics VII 6
4 F18L3W035 Linear Algebra and Applications VII 6
5 F18L3W008 Introduction to Data Science VII 6
6 F18L3W161 Social Networks and Media VII 6
7 F18L3W108 Internet of Things VII 6
8 F18L3W004 Databases VII 6
9 F18L3W068 Computing in the Cloud VII 6
10 F18L3S036 Machine learning VIII 6
11 F18L3S150 Data Mining VIII 6
12 F18L3S163 Statistical Modeling VIII 6
13 F18L3S157 Data warehousing and analytics VIII 6
14 F18L1S023 Business Statistics VIII 6
15 F18L3W076 Introduction to time series analysis VIII 6

 

Table 3: List of Postgraduate Courses in Statistics for Data Analysis

РБ CODE / Subject Semester M / E ECTS
1 SNP-Z-1 Data analysis with statistical packages IX M 6
2 SDP-Z-3 Bayesian data analysis IX M 6
3 SDP-Z-4 Data preparation and research IX M 6
4 Elective item from Table 4 IX E 6
5 Elective item from Table 4 IX E 6
6 SNP-Z-2 Regression Models X M 6
7 Elective item from Table 4 X E 6
8 Master Thesis X M 18

 

Table 3 lists the electives from the Statistics for Data Analysis study program. In addition to these courses, the student can choose from all elective courses, defined for all study programs, from the second cycle that are serviced by the faculty. It is allowed to choose one elective subject from the university list nand free electives.

 

 

Table 4: Optional list of offered items

РБ New code /   Subject Semester ECTS
1 Methods of statistical locking IX 6
2 Concepts and application of big data IX 6
3 Analysis and forecasting time series IX 6
4 Advanced algorithms IX 6
5 Modeling and fusing IX 6
6 Information Processing in Biological Systems IX 6
7 Analysis of data from related systems IX 6
8 Text Data Processing IX 6
9 Optimization methods IX 6
10 Data processing in bioinformatics IX 6
11 Network Analysis IX 6
12 Ambiental intelligence IX 6
13 Web of the Future IX 6
14 Statistical Programming X 6
15 Statistical Learning X 6
16 Multidimensional statistical analysis X 6
17 Numerical methods for data science X 6
18 Statistical research skills: editing , reporting and visualization of data X 6
19 Business Analytics X 6
20 Random processes X 6
21 Big Data Modeling and Management X 6
22 Discovering knowledge in big graph data X 6
23 Open and related data X 6
24 Modern Simulations and Modeling X 6
25 Computational paradigms in the Internet of Things X 6
26 Data analysis from mobile sensors / sources X 6
27 Intelligent mobile applications X 6

 

The student can choose a subject from the list of offered elective courses from all study programs of the second cycle of studies. The list of offered electives can be found on this   link .

Табови

д-р Дејан Ѓорѓевиќ

Табови

д-р Сузана Лошковска

Dear Colleagues,

On behalf of the organizing committee, we are pleased to invite you to submit your latest work to the 18th ICT Innovations Conference, taking place in the scenic city of Struga, on the shores of Lake Ohrid, from September 26–28, 2026.

As a leading international platform in the region, ICT Innovations brings together academics and industry practitioners to share recent research, practical solutions, and experiences, and to discuss trends, opportunities, and challenges at the forefront of computer science and engineering.

Conference Venue

The conference will be held at Hotel Drim, located in the center of Struga, on the shores of Lake Ohrid, a UNESCO World Heritage site known for its natural beauty and cultural heritage.

Conference Theme

Intelligent Horizons: AI and Digital Technologies Reshaping Society

AI and digital technologies are redefining the boundaries of what is possible — across industry, healthcare, governance, education, and everyday life. As these technologies mature at an unprecedented pace, understanding both their potential and their consequences becomes essential.

This year's theme invites researchers, practitioners, and thought leaders to share cutting-edge work that advances innovation while addressing the societal, ethical, and practical dimensions of intelligent systems.

Topics of Interest

Topics of interest include, but are not limited to:

  • AI and Society
  • AI in Industry and Innovation
  • Computational Advances in AI
  • The Future of Human-AI Collaboration
  • Data Science and Predictive Analytics
  • Software Engineering and Development Tools
  • Theoretical Computer Science
  • Cloud, Edge, Parallel, and Distributed Computing

Publications & Indexing 

All accepted full papers will be included in the ICT Innovations 2026 Proceedings published by Springer in Communications in Computer and Information Science Series (CCIS).

The conference proceedings are indexed in Web of Science: Conference Proceedings Citation Index (CPCI), SCOPUS, SCImago, DBLP, Google Scholar, EI-Compendex and Mathematical Reviews.

Accepted short papers will be published in the ICT Innovations conference web proceedings.

Best Paper Award

To recognize outstanding contributions, the conference will present a Best Paper Award. The award will be selected by the Program Committee based on the originality, technical quality, and presentation of the work at the conference.

Paper Submission Guidelines

ICT Innovations 2026 invites submissions of original and unpublished research papers on all conference topics. We also welcome workshop proposals to be held alongside the main conference.

Submission requirements:

  • Paper length: max. 15 pages
  • Format: Springer LNCS/CCIS templates (LaTeX or Word)
  • Language: English
  • Review process: Single-blind peer review

The Program Committee includes over 200 scientists from more than 50 countries.

At least one author of each accepted paper must register for the conference. The corresponding author must complete and sign the Consent-to-Publish form on behalf of all authors. After submission to Springer, changes to authorship are not permitted.

Important Dates

  • Submission Deadline: June 15, 2026
  • Acceptance Notification: July 15, 2026
  • Camera-Ready Submission: August 15, 2026
  • Early Registration Deadline: August 30, 2026
  • Conference Dates: September 26–28, 2026

For further information, or to propose workshops/special sessions, please visit our website at http://ictinnovations.org or contact us at ict.innovations@finki.ukim.mk.

We would appreciate it if you could forward this call to colleagues and students who may be interested.

 

Kind regards,

Prof. Igor Mishkovski

Prof. Sasho Gramatikov

Prof. Biljana Risteska Stojkoska

Co-chairs, ICT Innovations 2026 Conference

11/03/2026
Wednesday, March 11, 2026

Statistics for data analysis (4+1)

1. General Information

This program is designed to train staff with solid statistical knowledge with a focus on the newly recognized field of data science. The curriculum combines rigorous statistical theory with broader practical experience in applying statistical models to data work. Graduates will be in high demand. Most students are expected to be employed as statisticians, analysts and data experts within private and public institutions providing statistical consultations.

  • Name of the proposer: University "Ss. Cyril and Methodius University in Skopje, Faculty of Information Sciences and Computer Engineering - FINKI
  • Title of the study program: Second cycle academic studies in Statistics for Data Analysis
  • Scientific-research area: technical-technological / natural mathematical
  • Field: Informatics / Mathematics
  • Areas: Mathematical Statistics and Operations Research, Data Processing, Applied Mathematics and Mathematical Modeling, Programming, Artificial Intelligence, Algorithms, Information Processing .
  • The value of postgraduate studies is 60 ECTS credits.
  • Duration of studies: 2 semesters .
  • One academic year consists of two semesters lasting 30 weeks (1 semester = 15 weeks).
  • Conditions for enrollment : according to the competition announced by the university, completed undergraduate studies in information science, computer or related fields with a minimum of 240 credits.
  • First semester: 3 compulsory courses and 2 electives, one of which may be from the University list.
  • Second semester : 1 compulsory and 1 elective course and completed project - master's thesis of 18 ECTS.
  • 1 ECTS credit corresponds to 30 hours of total work engagement.
  • The number of contact hours is 4.
  • The academic title or degree obtained upon completion of the studies is Master of Information Science - Statistics in Data Analysis

                Master of Science in Informatics - Statistics for Data Analytics

 

2. Studies

Table 2: List of Postgraduate Courses in Statistics for Data Analysis

РБ CODE / Subject Semester M / E ECTS
1 SNP-Z-1 Data analysis with statistical packages IX M 6
2 SDP-Z-3 Bayesian data analysis IX M 6
3 SDP-Z-4 Data preparation and research IX M 6
4 Elective item from Table 4 IX E 6
5 Elective item from Table 4 IX E 6
6 SNP-Z-2 Regression Models X M 6
7 Elective item from Table 4 X E 6
8 Masterrska topic X M 18

 

Table 3 shows the electives from the study program Statistics for Data Analysis. In addition to these courses, the student can choose from all elective courses, defined for all study programs, from the second cycle that are serviced by the faculty. It is allowed to choose one elective course from the university list of free elective courses.

 

 

Table 3: Optional list of offered items

РБ New code /   Subject Semester ЕКТС
1 Methods of statistical locking IX 6
2 Concepts and application of big data IX 6
3 Analysis and forecasting time series IX 6
4 Advanced Algorithms IX 6
5 Modeling and fusing IX 6
6 Information Processing in Biological Systems IX 6
7 Analysis of data from related systems IX 6
8 Text Data Processing IX 6
9 Optimization methods IX 6
10 Data processing in bioinformatics IX 6
11 Network Analysis IX 6
12 Ambiental intelligence IX 6
13 Web of the Future IX 6
14 Statistical programming X 6
15 Statistical Learning X 6
16 Multidimensional statistical analysis X 6
17 Numerical methods for data science X 6
18 Statistical research skills: editing , reporting and visualization of data X 6
19 Business Analytics X 6
20 Random processes X 6
21 Big Data Modeling and Management X 6
22 Discovering knowledge in big graph data X 6
23 Open and related data X 6
24 Modern Simulations and Modeling X 6
25 Computational paradigms in the Internet of Things X 6
26 Data analysis from mobile sensors / sources X 6
27 Intelligent mobile applications X 6

 

The student can choose a subject from the list of offered elective courses from all study programs of the second cycle of studies. The list of offered electives can be found on this   link .