High Performance Computing and Big Data Analytics Programme Profile

High Performance Computing and Big Data Analytics master presentation

Educational Programme High Performance Computing and Big Data Analytics
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 Assoc. Professor Virginia Niculescu
Phone +40 264 405327
Fax +40 264 591906
E-mail vniculescu@cs.ubbcluj.ro
Profile of the Degree Programme High Performance Computing and Big Data Analytics degree program
Target Group / Addressees Graduates in Mathematics, Computer Science, Economics, Electrical/Electronic Engineering, Physics, Chemistry, Biology, etc.
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:

  • Machine Learning
  • Parallel and Distributed Operating Systems
  • Formal Modelling of Concurrency
  • Advanced Methods in Data Analysis
  • Big Data Processing
  • Models in parallel programming
  • General Purpose GPU Programming
  • Workflow Systems
  • Data streaming
  • Data Mining
  • Grid, Cluster and Cloud Computing
  • Knowledge Discovery in Wide Area Networks
Purposes of the Programme Programme objectives:

  • Acquisition of theoretical knowledge in complex systems modelling based on mathematical concepts and methods, and on programming concepts and techniques.
  • Acquisition of applicative and practical knowledge. in programming and usage on/of computation systems, especially those of high performance, which are necessary for solving real life problems and for simulating complex problem solutions.
  • Acquisition of theoretical, applicative knowledge in exploitation (data-analysis, knowledge-discovering) and visualization of „big data” for computation problems, statistical interpretations, decision processes, or for scientific instruments.
  • Acquisition of theoretical, applicative and practical knowledge in applicative scientific domains where high performance systems are used.
  • Acquisition of theoretical, applicative and practical knowledge in analysis and improvement of software processes.
  • Acquisition of theoretical, applicative and practical knowledge in professional modelling for team work as well as interdisciplinary approaches to research and development.
Specialization / Area of Expertise High Performance Computing, Parallel and Distributed Programming, Modelling in Computer Science, Data Analysis.
Extra Peculiarities Optional: Practice of Education.
Practical Training In the 2nd year (4th semester) of the program the students participate in a research project and also in an internship in the field of High Performance Computing and Big Data Analytics.
Final Examinations Dissertation thesis
Gained Abilities and Skills General competences:

  • Advanced knowledge of theoretical, methodological, and practical developments in computer science;
  • Systematic use of computer science knowledge to model and interpret new situations, within application contexts larger than the known ones;
  • Detailed knowledge and integrated use of conceptual and methodological apparatus pertaining to informatics to provide solutions for incompletely defined situations, to solve new theoretical and practical problems;
  • Proficient use of verification, validation, and evaluation criteria and methods to his/her own software solutions, ability to formulate value judgments and to justify/explain constructive decisions;
  • Use advanced skills to develop and conduct complex software projects, of practical and/or research nature, using a wide range of quantitative and qualitative methods;
  • Advanced communication skills within different professional environments, appropriate use of computer science vocabulary, good English knowledge;
  • Team work abilities, assuming different execution and leading roles, performing professional tasks with considerable amounts of autonomy and responsibility.

Specialty competences:

  • Capability of developing of high performance programs based on parallel and distributed programming;
  • Assimilation of mathematical concepts and formal models to understand the methods and components of high performance systems;
  • Analysis, design, and implementation of data analysis systems;
  • Understanding and acquisition of methods of modelling, optimization, analysis of massive datasets, data visualization;
  • Demonstrate advanced skills to analysis, design, and construction of software systems, using a wide range of hardware / software platforms, programming languages and environments, and modelling, verification and validation tools;
  • Demonstrate advanced skills to apply methods for data analysis and processing, data mining, pattern recognition;
  • Demonstrate advanced modelling skills for economic, industrial, scientific phenomena and processes;
  • Ability to teach students in high schools computer science concepts and theories, provided that the holder of the dissertation diploma owns a graduation certificate of the pedagogical education module.
Job Placement, Potential Field of Professional Activity Software companies: analyst, software project/servicesmanager, developer, tester positions.
Universities or research institutions: research assistant, researcher, consultant, academic positions.
Companies with activities in domains that uses high performance computing and big data analytics: software expert.