Data Analysis and Modelling Programme Profile

Educational Programme Data Analysis and Modelling
Degree Awarded Master in Computer Science
Standard Length of Studies
(Number of ECTS Credits)		 2 years – 4 semesters – 120 ECTS
Type of Study Full-time
Higher Education Institution Babeş-Bolyai University
Faculty / Department Faculty of Mathematics and Computer Science
Contact Person Professor Csató Lehel
Phone +40 264 405327
Fax +40 264 591906
E-mail lehel.csato@cs.ubbcluj.ro
Profile of the Degree Programme Data Analysis and Modelling
Target Group / Addressees Graduates in Computer Science, Mathematics, Electrical/Electronic Engineering, Physics, Data analysts
Entrance Conditions Graduate student recruitment is achieved by competition. The overall three/four-year undergraduate average grade and a personal portfolio, for candidates with a Bachelor Degree in Computer Science, Computer Mathematics or Economical Computer Science and respectively the grade of a written test (see the curricula here) and a personal portfolio, for candidates outside the above mentioned areas.
Further Education Possibilities The master’s program aims at providing students with the appropriate tools for further doctoral studies and professional activity.
Description of Study Core disciplines. We aim for advanced lectures in the topics of massive data analysis, machine learning, information mining and text mining, respectively simulation and modelling.Main modules:

  • Analysing big data-sets,
  • Intelligent data warehouses and applications,
  • Parallel programming paradigms,
  • Machine learning,
  • Applied robotics,
  • Research methodology,
  • Monte Carlo Modelling,
  • Information retrieval,
  • Natural language processing,
  • Text mining.
Purpose of the Programme Programme objectives:

  • Acquisition of core theory and working knowledge of methods required when analysing large amount of data that is collected automatically;
  • Acquisition specialised knowledge in modelling, design and implementation of complex systems based on simulation and visualisation;
  • Acquisition of the theoretical support to solve complex problems;
  • Acquisition of knowledge of information extraction methods;
  • Working knowledge of practical solutions to information extraction and their practical exploitation.
Specialization /
Area of Expertise		 Data analysis, Machine learning, Modelling and simulation in applied computer science, Model-based simulation, Information extraction,
Optional modules Modelling using agents, Cryptography, Game theory, Robotics
Practical Training In the 2nd year (4th semester) of the program the students can choose to take part in research work – under faculty supervision – or to work at a partner company towards their MSc dissertation thesis.
Final Examinations Dissertation thesis
Acquired Abilities and Skills General competences:

  • Understanding and working with basic concepts of data analysis and modelling;
  • Capability of analysis and synthesis;
  • Modelling and solving real-life problems;
  • Capability to analyse, present, and argument modelling and simulation results;
  • Use of specialised data analytics vocabulary, good English knowledge;

Speciality competences:

  • Assimilation of mathematical concepts and formal models to understand the methods and components of data processing and analytics;
  • Analysis, design, and implementation of data analysis systems;
  • Understanding and acquisition of methods of machine learning, modelling and simulation, analysis of massive datasets, processing textual data.
Job Placement, Potential Field of Professional Activity Experts in software companies, developer positions, tester positions