Form of study

Full-time study

Length of study

2 years (4 semesters, 120 ECTS)

Admission requirements

1.      English Proficiency: Candidates must demonstrate proficiency in English.

2.      Academic Background:

·      for candidates from the same field: completion of the minimum curriculum  indicated in the study plan. The minimum curriculum is specified for candidates from the same domain of study (Computer Science, Information Technology, or related domains). and includes foundational courses: Numerical Methods, Data Structures and Algorithms, Object Oriented Programming, Procedural Programming, Interactive programming (Python), Human-computer interaction (HCI)

·      for candidates from different fields: Candidates must complete the required prerequisites before admission or during the program through designated bridging modules.

Eligibility for English proficiency exemptions

1.    Candidates holding degrees or certificates from universities where English was study language.

2.  Candidates with an English language certificate of at least C1 level.

Aim of the study program

This master’s program is designed to equip students with the knowledge, skills, and competencies required for successful careers as data analysts or data researchers in the field of data science. The curriculum emphasizes three core areas:

1        Programming languages: Mastery of languages for data management and the development of data warehouses.

2        Data visualization tools: Proficiency in visual data exploration through charts, dashboards, infographics, and reporting techniques using specialized software.

3        Statistical and dimensional analysis: Expertise in predictive statistical analysis and dimensional analysis methods.

Additionally, the program provides in-depth training in machine learning algorithms, covering topics such as data reduction and prediction techniques, natural language processing, neural networks, and other advanced AI methodologies.

Acquired competencies

The Master’s Program in Data Science is designed to equip students with advanced knowledge and skills in extracting, processing, and analyzing large-scale data.

By the end of the program, students will have acquired the following competencies:

Ø  Advanced Analytical and Statistical Skills (proficiency in statistical modeling, hypothesis testing, and predictive analytics, ability to interpret complex data patterns and derive meaningful insights, expertise in implementing machine learning and deep learning algorithms.)

Ø  Programming and Technical Proficiency (advanced skills in programming languages such as Python, R, and SQL, ability to use specialized tools and libraries, familiarity with Big Data frameworks).

Ø  Data Engineering and Management (competence in designing and managing scalable data pipelines, knowledge of cloud computing platforms, proficiency in database systems and data warehouses.)

Ø  Domain-Specific Knowledge (application of data science methods in various industries, including finance, healthcare, and technology).

Ø Critical Thinking and Problem-Solving (competence in designing experiments and testing data-driven hypotheses).

Ø Communication and Visualization (expertise in creating impactful visualizations using tools like Tableau, Power BI, or Matplotlib).

ØResearch and Innovation (capacity to conduct independent research and contribute to academic or industrial innovation, ability to write and publish high-quality research papers or technical reports).

Studied disciplines

1^st Semester: Scientific research methodology, Exploratory data analysis, Machine Learning and Data Mining, Mathematical models and optimizations, Computational statistics

2^nd Semester: Big Data technologies, Neural networks and Deep Learning, Large language models, Cloud applications, Software security, Data analysis tools.

3^rd Semester: Data science and analysis, Data analysis and visualization, Application-oriented software environments, Software project management Data Science.

4^th Semester: Research Practice, Master’s Thesis Preparation, Defense of the Master’s Thesis

State final examination

Master’s studies are completed by defending the Master’s thesis

Career opportunities

Graduates of the study program benefit from real employment opportunities, both within companies in the field of Information and Communications Technologies (ICT), as well as in any other public or private sector of the economy where data analysis and processing are used. The employment field in this area is developing.

Possible occupations for graduates of the Data Science master’s degree program, according to the Classification of Occupations of the Republic of Moldova are:  Database Administrator, Database Architect, IT Project Manager, Data Operations Manager, e-Government Specialist, Software Designer, Software Engineer, Programmer, Data analyst, University Assistant, University Lecturer, Secondary School Teacher, High School Teacher.