Type:
Bachelor
Speciality:
056201.02.6 - Statistics
Specialisation:
056201.02.6 - Applied statistics and data science
Qualification awarded:
Bachelor of Applied Statistics and Data Science
Programme academic year:
2025/2026
Mode of study:
Full time
Language of study:
Հայերեն
1. Admission criteria/requirements
The admission is carried out in accordance with the decision of the Government of the Republic of Armenia No. 476-N of April 7, 2022 on “Procedure for Admission to State and Non-State Higher Education Institutions of the Republic of Armenia (Bachelor’s Degree Programme)”,
Entrance exams:
1. Mathematics - competitive
2. Physics or English - competitive
3. Armenian Language and Literature - non-competitive
Entrance exams:
1. Mathematics - competitive
2. Physics or English - competitive
3. Armenian Language and Literature - non-competitive
2. Programme Objectives
· To prepare applied statisticians and data analysts for entry-level positions in business, engineering, and biotechnology who are capable of applying statistical methods and computational tools to solve complex problems in broad and well-defined contexts.
· To develop analytical and problem-solving skills for processing and interpreting data sets, enabling graduates to contribute with a certain degree of autonomy to data-driven decision-making in diverse professional environments.
· To develop analytical and problem-solving skills for processing and interpreting data sets, enabling graduates to contribute with a certain degree of autonomy to data-driven decision-making in diverse professional environments.
3. Educational outcomes of the programme
Upon completion of the course, the student will be able to:
- Present the fundamental principles of various mathematical disciplines required to solve problems in data science.
- Demonstrate knowledge and understanding of the main theories and principles of probability theory and mathematical statistics, applying them in broad contexts.
- Explain the fundamental principles of modelling-oriented software system development for data analysis in well-defined cases.
- Apply probabilistic and statistical methods in scientific and applied contexts, demonstrating analytical skills.
- Evaluate statistical and machine learning methods for data analysis, processing, and decision-making in various professional fields, identifying their main strengths and weaknesses.
- Analyze and interpret probabilistic and statistical models in diverse scientific applications, demonstrating awareness of their limitations in broad contexts.
- Demonstrate knowledge of ethical principles and legal frameworks governing data collection, analysis, and use, applying them to ensure trustworthy practices in applied statistics and data science.
- Apply mathematical methods in diverse but predictable professional contexts, demonstrating problem-solving skills.
- Build basic statistical models to address challenges in various domains, analyze datasets, and draw evidence-based conclusions with a degree of innovation.
- Develop algorithms using programming tools to solve specific problems while managing project components with guided autonomy.
- Use mathematical programming packages (e.g., Python, R) to solve theoretical and applied problems in structured scenarios.
- Apply methods of mathematical statistics to economic problems, taking responsibility for decision-making in well-defined economic contexts.
- Develop and implement data visualization methods using programming tools to effectively present statistical results in professional reports and presentations.
- Access and critically evaluate various sources to obtain necessary information for tasks in broad contexts.
- Organize, analyze, and synthesize professional information to draw reasoned conclusions in well-defined environments.
- Process data, make decisions with a certain degree of autonomy, participate in discussions, defend arguments, and effectively present results.
- Communicate within the professional community in both written and oral forms in native and foreign languages.
4. Assessment methods
Assessment includes the following components:
1. Evaluation of mastery of course (module) during the semester (2 midterm exams).
2. Current assessment of individual topics during the semester.
3. Evaluation of independent assignments (individual work).
4. Evaluation of independent and/or group research work during the semester (research can replace one of the midterms).
5. Assessment of class participation.
6. Final assessment during the examination period evaluating the level of achievement of the intended learning outcomes of the course (module).
Courses are divided into four groups according to the form of assessment:
· With final examination,
· Without final examination,
· Without midterm examinations,
· Check-up based.
1. Evaluation of mastery of course (module) during the semester (2 midterm exams).
2. Current assessment of individual topics during the semester.
3. Evaluation of independent assignments (individual work).
4. Evaluation of independent and/or group research work during the semester (research can replace one of the midterms).
5. Assessment of class participation.
6. Final assessment during the examination period evaluating the level of achievement of the intended learning outcomes of the course (module).
Courses are divided into four groups according to the form of assessment:
· With final examination,
· Without final examination,
· Without midterm examinations,
· Check-up based.
5. Graduates future career opportunities
Graduates may work:
· In any bank, particularly in risk management departments, as financial and statistical analysts.
· In institutions (public bodies, commercial organizations, etc.) that have analytical or information technology departments.
· In any bank, particularly in risk management departments, as financial and statistical analysts.
· In institutions (public bodies, commercial organizations, etc.) that have analytical or information technology departments.
6. Resources and forms to support learning
· Printed, electronic, and online literature
· Computer laboratories
· Computer laboratories
7. Educational standards or programme benchmarks used for programme development
1. National Qualifications Framework of the Republic of Armenia (Government Decision No. 714-N of July 7, 2016).
2. Sectoral Qualifications Framework for “Mathematics” (2022).
3. European Qualifications Framework (2008).
Bachelor’s degree programmes in Applied Statistics and Data Science from Moscow, Saint Petersburg, and leading Western universities were used as benchmarks.
2. Sectoral Qualifications Framework for “Mathematics” (2022).
3. European Qualifications Framework (2008).
Bachelor’s degree programmes in Applied Statistics and Data Science from Moscow, Saint Petersburg, and leading Western universities were used as benchmarks.
8. Requirements for the academic staff
1. General Competencies
Teaching/Pedagogical:
· Ability to design a course syllabus (calendar plan).
· Knowledge of interactive teaching methods and ability to apply active learning techniques.
Research:
· Ability to work with diverse scientific sources and online information resources.
· Ability to supervise student research groups.
Communication:
· Oral communication skills.
· Ability to present research results in written form.
· Knowledge of a professional foreign language.
ICT Skills:
· Proficiency in MS Office (Word, Excel, PowerPoint).
· Ability to prepare and deliver presentations.
Other Competencies:
· Adherence to professional ethical standards.
· Ability to assess necessary resources and implement programmes effectively.
· Time management and planning skills.
2. Professional Competencies
· Mastery of the professional courses of the Bachelor’s programme in Statistics.
· Thorough knowledge of the taught module.
· Understanding of the key concepts of related modules.
· Ability to incorporate a research component into the course.
· Ability to supervise final theses.
3. General Requirements
Academic Degree:
· Academic degree or Master’s degree in statistics or a related field.
· At least 2 scientific and/or methodological publications within the last 5 years.
· Participation in conferences and/or workshops within the last 5 years.
Teaching Experience:
· Participation in local or international professional development or qualification enhancement courses within the last 5 years.
Other Requirements:
· Compliance with YSU Code of Conduct regulations.
· Average student evaluation score of at least 4.0 (for teaching professors).
Teaching/Pedagogical:
· Ability to design a course syllabus (calendar plan).
· Knowledge of interactive teaching methods and ability to apply active learning techniques.
Research:
· Ability to work with diverse scientific sources and online information resources.
· Ability to supervise student research groups.
Communication:
· Oral communication skills.
· Ability to present research results in written form.
· Knowledge of a professional foreign language.
ICT Skills:
· Proficiency in MS Office (Word, Excel, PowerPoint).
· Ability to prepare and deliver presentations.
Other Competencies:
· Adherence to professional ethical standards.
· Ability to assess necessary resources and implement programmes effectively.
· Time management and planning skills.
2. Professional Competencies
· Mastery of the professional courses of the Bachelor’s programme in Statistics.
· Thorough knowledge of the taught module.
· Understanding of the key concepts of related modules.
· Ability to incorporate a research component into the course.
· Ability to supervise final theses.
3. General Requirements
Academic Degree:
· Academic degree or Master’s degree in statistics or a related field.
· At least 2 scientific and/or methodological publications within the last 5 years.
· Participation in conferences and/or workshops within the last 5 years.
Teaching Experience:
· Participation in local or international professional development or qualification enhancement courses within the last 5 years.
Other Requirements:
· Compliance with YSU Code of Conduct regulations.
· Average student evaluation score of at least 4.0 (for teaching professors).
9. Additional information about the programme
The Bachelor’s degree programme is exceptional as no other higher education institution in Armenia offers a similar one in Applied Statistics. Related programmes exist only at the master’s degree level.
There are 8 state-funded places available in the programme.
There are 8 state-funded places available in the programme.
