Educational level (Bachelor/Master):
Master
Programme title and code of specialty:
031101.19.7 - Economics
Specialization:
031101.19.7 - Data Science for Business
Programme start academic year:
2025/2026
Mode of study (full time/part time):
Full time
Language of study:
諃铡盏榨謤榨斩
1. 91视频 requirements (criteria)
Applicants to the Master鈥檚 Program in 鈥淒ata Science in Business鈥 at the Faculty of Economics and Management of Yerevan State University may include graduates holding a Bachelor鈥檚, Master鈥檚, or Diploma Specialist qualification from state higher education institutions of the Republic of Armenia, as well as from non-state institutions that have received programmatic or institutional accreditation.
Applicants to the program must hold a Bachelor鈥檚 degree or an equivalent qualification. 91视频 of applicants with both relevant and non-relevant academic backgrounds (holding a Bachelor鈥檚 degree or equivalent qualification) is conducted exclusively on a competitive basis, determined by the results of entrance examinations.
91视频 is carried out in accordance with the regulations governing admission to full-time Master鈥檚 programs at Yerevan State University.
Applicants to the program must hold a Bachelor鈥檚 degree or an equivalent qualification. 91视频 of applicants with both relevant and non-relevant academic backgrounds (holding a Bachelor鈥檚 degree or equivalent qualification) is conducted exclusively on a competitive basis, determined by the results of entrance examinations.
91视频 is carried out in accordance with the regulations governing admission to full-time Master鈥檚 programs at Yerevan State University.
2. Programme Objectives
The aim of the program is to prepare competitive professionals in machine learning and artificial intelligence who meet the demands of the global labor market. Graduates will be capable of working as data scientists and economic analysts, as well as conducting advanced research using structured and unstructured data and proposing data-driven solutions based on their analysis.
3. Programme learning outcomes
Upon completion of the course, the student will be able to:
- Professional knowledge and understanding
- 諉榨謤寨铡盏铡謥斩榨宅 謬 瞻铡沾榨沾铡湛榨宅 摘铡沾铡斩铡寨铡寨斋謥 瞻铡站铡斩铡寨铡斩铡盏斋斩, 謪蘸湛斋沾斋咋铡謥斋崭斩, 站斋粘铡寨铡眨謤铡寨铡斩, 乍寨崭斩崭沾榨湛謤斋寨 謬 铡盏宅 沾榨诈崭栅斩榨謤斋 寨斋謤铡占崭謧诈盏崭謧斩斩榨謤炸謮
- 諉榨謤寨铡盏铡謥斩榨宅 沾铡战斩铡眨斋湛铡謥站铡债 瞻铡沾铡寨铡謤眨展铡盏斋斩 债謤铡眨謤榨謤炸 (R, Java, SQL, Pyton)謮
- 諑榨謤宅崭謧债榨宅 謫斋斩铡斩战铡寨铡斩 辗崭謧寨铡斩榨謤炸 謬 闸斋咋斩榨战 眨崭謤债炸斩诈铡謥斩榨謤炸諠 謪眨湛铡眨崭謤债榨宅崭站 湛站盏铡宅铡眨斋湛崭謧诈盏铡斩 沾榨诈崭栅斩榨謤炸, 占榨斋斩摘斋斩斋謤斋斩眨炸, 铡站湛崭沾铡湛铡謥崭謧沾炸 謬 沾崭斩斋诈崭謤斋斩眨炸謮
- Practical professional skills
- 曰謤铡寨铡斩铡謥斩榨宅 沾榨债 湛站盏铡宅斩榨謤斋 瞻铡站铡謩铡眨謤崭謧沾, 沾辗铡寨崭謧沾 謬 站榨謤宅崭謧债崭謧诈盏崭謧斩謮
- 钥斋謤铡占榨宅 铡謤瞻榨战湛铡寨铡斩 闸铡斩铡寨铡斩崭謧诈盏铡斩 (AL) 謬 沾榨謩榨斩铡盏铡寨铡斩 崭謧战崭謧謥沾铡斩 沾崭栅榨宅斩榨謤謮
- 钥斋謤铡占榨宅 闸斋咋斩榨战崭謧沾 謬 瞻榨湛铡咋崭湛崭謧诈盏崭謧斩斩榨謤崭謧沾 站斋咋崭謧铡宅斋咋铡謥斋铡盏斋 沾榨诈崭栅斩榨謤 謬 栅謤铡斩謥 瞻斋沾铡斩 站謤铡 蘸铡湛謤铡战湛榨宅 瞻铡辗站榨湛站崭謧诈盏崭謧斩斩榨謤謮
- General (transferable) competences
- 諘眨湛铡眨崭謤债榨宅 沾铡诈榨沾铡湛斋寨铡寨铡斩 謬 站斋粘铡寨铡眨謤铡寨铡斩 沾榨诈崭栅斩榨謤炸 沾铡战斩铡眨斋湛铡寨铡斩 謬 寨斋謤铡占铡寨铡斩 窄斩栅斋謤斩榨謤斋 宅崭謧债沾铡斩 瞻铡沾铡謤謮
- 諘眨湛站榨宅 湛铡謤铡湛榨战铡寨 铡詹闸盏崭謧謤斩榨謤斋謥` 铡斩瞻謤铡摘榨辗湛 湛榨詹榨寨铡湛站崭謧诈盏崭謧斩炸 战湛铡斩铡宅崭謧 瞻铡沾铡謤, 斋斩展蘸榨战 斩铡謬 瞻铡沾铡寨铡謤眨榨宅, 站榨謤宅崭謧债榨宅 謬 寨铡湛铡謤榨宅 瞻榨湛謬崭謧诈盏崭謧斩斩榨謤 战湛铡謥站铡债 湛站盏铡宅斩榨謤崭站謮
- 曰謤铡寨铡斩铡謥斩榨宅 沾斋栈眨斋湛铡寨铡謤眨铡盏斋斩 瞻榨湛铡咋崭湛崭謧诈盏崭謧斩斩榨謤, 崭謤崭斩謩 蘸铡瞻铡斩栈崭謧沾 榨斩 眨斋湛崭謧诈盏铡斩, 湛榨窄斩斋寨铡盏斋 謬 湛斩湛榨战崭謧诈盏铡斩 湛铡謤闸榨謤 闸斩铡眨铡站铡占斩榨謤斋 湛站盏铡宅斩榨謤斋 瞻铡站铡謩铡眨謤崭謧沾 謬 瞻铡沾铡栅謤崭謧沾, 斋斩展蘸榨战 斩铡謬 窄崭謤炸 站榨謤宅崭謧债崭謧诈盏崭謧斩謮
- 諍铡湛謤铡战湛榨宅 咋榨寨崭謧謥崭謧沾斩榨謤, 斩榨謤寨铡盏铡謥斩榨宅 瞻榨湛铡咋崭湛崭謧诈盏崭謧斩斩榨謤斋 铡謤栅盏崭謧斩謩斩榨謤炸 謬 站铡謤榨宅 眨斋湛铡寨铡斩 闸铡斩铡站榨粘榨謤:
4. Assessment methods
At Yerevan State University, a multi-component system of continuous assessment and evaluation of students鈥 knowledge is implemented, which includes the following components:
1. assessment of the acquisition of course (educational module) subcomponents during the semester (two midterm exams),
2. ongoing assessments of individual course (educational module) topics during the semester,
3. evaluation of the completion and mastery of independent assignments 锌褉械写褍褋屑芯褌褉ed by the program (independent work),
4. assessment of independent and/or group research work 锌褉械写褍褋屑芯褌褉ed by the program during the semester (research work, which replaces one of the midterm exams),
5. evaluation of class participation (participation),
6.final assessment of the entire course (educational module) during the examination period, which involves evaluating the level of achievement of the intended learning outcomes of the course.
Based on the workload of courses (educational modules) 锌褉械写褍褋屑芯褌褉ed by the curriculum, the form of instruction, teaching methods, and the importance of the course in shaping students鈥 professional knowledge and skills, courses are classified into four groups according to the assessment format:
1.with a final assessment,
2.without a final assessment,
3.without midterm examination assessment,
4.pass/fail (credit-based).
1. assessment of the acquisition of course (educational module) subcomponents during the semester (two midterm exams),
2. ongoing assessments of individual course (educational module) topics during the semester,
3. evaluation of the completion and mastery of independent assignments 锌褉械写褍褋屑芯褌褉ed by the program (independent work),
4. assessment of independent and/or group research work 锌褉械写褍褋屑芯褌褉ed by the program during the semester (research work, which replaces one of the midterm exams),
5. evaluation of class participation (participation),
6.final assessment of the entire course (educational module) during the examination period, which involves evaluating the level of achievement of the intended learning outcomes of the course.
Based on the workload of courses (educational modules) 锌褉械写褍褋屑芯褌褉ed by the curriculum, the form of instruction, teaching methods, and the importance of the course in shaping students鈥 professional knowledge and skills, courses are classified into four groups according to the assessment format:
1.with a final assessment,
2.without a final assessment,
3.without midterm examination assessment,
4.pass/fail (credit-based).
5. Graduates career opportunities
The potential employment opportunities and career paths of the program graduates are determined by students鈥 preferences as well as the specific characteristics of the field. In general, the program is designed to prepare specialists in mathematical modeling and data science, regardless of the sector or size of the organization.
In particular, below is a list of organizations where graduates of the Program may find employment:
the Government of the Republic of Armenia, RA ministries and other state agencies, the Central Bank of the Republic of Armenia, commercial banks in Armenia, insurance companies, credit organizations, investment companies and pension funds, local branches of international corporations, as well as other international organizations, including Picsart, SmartClickAI, WorldQuant, Krisp, Plat.ai, ServiceTitan, Webb Fontaine, PMI Science, Cognaize, Synopsys Armenia, NVIDIA, DataMotus, Sololearn, DataArt, VivaCell-MTS, Armenia Telephone Company OJSC, Ucom LLC, GNC-ALFA OJSC, Karas National Food Chain, Coca-Cola Hellenic Armenia, Grand Holding, and others.
It should also be noted that the majority of students are already employed in the aforementioned organizations after the first semester, applying the practical knowledge and skills acquired during the program.
In particular, below is a list of organizations where graduates of the Program may find employment:
the Government of the Republic of Armenia, RA ministries and other state agencies, the Central Bank of the Republic of Armenia, commercial banks in Armenia, insurance companies, credit organizations, investment companies and pension funds, local branches of international corporations, as well as other international organizations, including Picsart, SmartClickAI, WorldQuant, Krisp, Plat.ai, ServiceTitan, Webb Fontaine, PMI Science, Cognaize, Synopsys Armenia, NVIDIA, DataMotus, Sololearn, DataArt, VivaCell-MTS, Armenia Telephone Company OJSC, Ucom LLC, GNC-ALFA OJSC, Karas National Food Chain, Coca-Cola Hellenic Armenia, Grand Holding, and others.
It should also be noted that the majority of students are already employed in the aforementioned organizations after the first semester, applying the practical knowledge and skills acquired during the program.
6. Resources and forms to support learning
The following supporting resources are utilized in the learning process:
* laboratories equipped with modern hardware and software,
* electronic resources,
* the istc-ysu.ibmonthehub.com cloud platform.
* laboratories equipped with modern hardware and software,
* electronic resources,
* the istc-ysu.ibmonthehub.com cloud platform.
7. Educational standards and/or programme benchmarks used for the programme development
* The National Qualifications Framework of the Republic of Armenia, approved by the RA Government Decision No. 714-N of July 7, 2016.
* The Framework of Qualifications for the European Higher Education Area (2010).
* The Master鈥檚 Program in 鈥淒ata Analytics鈥 at San Jos茅 State University (USA).
* The Framework of Qualifications for the European Higher Education Area (2010).
* The Master鈥檚 Program in 鈥淒ata Analytics鈥 at San Jos茅 State University (USA).
8. Requirements for the academic staff
The teaching staff of the 鈥淒ata Science in Business鈥 educational program is continuously enriched with a new generation of young lecturers who are actively employed in the IT sector of the Republic of Armenia and are simultaneously members of the program team. Many of the instructors involved in the program are PhD students in the fields of data science and artificial intelligence. Some of them are graduates of the 鈥淒ata Science in Business鈥 Master鈥檚 program and recipients of PMI scholarships.
This process is of great importance, as it ensures the sustainability of the program. Young lecturers continuously incorporate the latest developments and innovative ideas from the Armenian IT sector into their courses, thereby fostering a strong and dynamic link between education and the labor market. The program鈥檚 young academic staff consists of early-career yet experienced professionals from both academia and the IT industry.
1. General Competencies
Teaching/Pedagogical Competencies
ability to design a course syllabus (course plan/calendar),
knowledge of interactive teaching methods and the ability to apply active learning techniques.
Research Competencies
ability to work with various academic sources, as well as to use online information resources,
ability to conduct sociological and marketing research,
ability to lead a research team.
Communication Competencies
ability to communicate orally with an audience,
ability to present research results in written form,
proficiency in at least one foreign language (minimum B1 level in English).
ICT Application Skills
basic computer skills (proficiency in MS Word, MS Excel, MS PowerPoint), as well as knowledge of programming tools such as Python/R, Java, and SQL,
ability to use modern social platforms,
skills in preparing and delivering presentations (e.g., pptx, Prezi, Canva, etc.).
Other Competencies
knowledge of professional ethics and legal norms regulating social relations,
ability to assess necessary resources and implement programs effectively,
time management and planning skills.
2. Professional Competencies
ability to effectively deliver the knowledge and skills defined in the course description,
ability to conduct professional research,
ability to develop and present economic strategies, as well as to plan and implement projects,
ability to work with widely used professional software tools,
ability to use various types of reports and conduct professional analysis based on them,
ability to present professional opinions both orally and in writing,
ability to address issues related to the professional field, including their management, organization, and financing.
3. General Requirements
Academic Qualifications
an academic degree and/or title in the fields of social sciences and data science, or, in certain cases, a Master鈥檚 degree in the relevant or a related field, including degrees obtained from foreign universities,
at least two scientific and/or methodological publications within the last five years, or practical experience in the field of data science,
participation in at least two conferences and/or workshops within the last five years, or participation in scientific-practical and/or professional events and competitions in the field of data science.
Teaching Experience
at least three years of experience in teaching professional courses and/or conducting training sessions (except for teaching practice by PhD students and courses related to programming language instruction),
participation in local or international training programs and/or professional development courses within the last five years (with the same exceptions as above).
Other Requirements
a teaching portfolio, including online materials for at least one-third of the courses taught,
an average student evaluation score of at least 3.5 (for current teaching staff).
This process is of great importance, as it ensures the sustainability of the program. Young lecturers continuously incorporate the latest developments and innovative ideas from the Armenian IT sector into their courses, thereby fostering a strong and dynamic link between education and the labor market. The program鈥檚 young academic staff consists of early-career yet experienced professionals from both academia and the IT industry.
1. General Competencies
Teaching/Pedagogical Competencies
ability to design a course syllabus (course plan/calendar),
knowledge of interactive teaching methods and the ability to apply active learning techniques.
Research Competencies
ability to work with various academic sources, as well as to use online information resources,
ability to conduct sociological and marketing research,
ability to lead a research team.
Communication Competencies
ability to communicate orally with an audience,
ability to present research results in written form,
proficiency in at least one foreign language (minimum B1 level in English).
ICT Application Skills
basic computer skills (proficiency in MS Word, MS Excel, MS PowerPoint), as well as knowledge of programming tools such as Python/R, Java, and SQL,
ability to use modern social platforms,
skills in preparing and delivering presentations (e.g., pptx, Prezi, Canva, etc.).
Other Competencies
knowledge of professional ethics and legal norms regulating social relations,
ability to assess necessary resources and implement programs effectively,
time management and planning skills.
2. Professional Competencies
ability to effectively deliver the knowledge and skills defined in the course description,
ability to conduct professional research,
ability to develop and present economic strategies, as well as to plan and implement projects,
ability to work with widely used professional software tools,
ability to use various types of reports and conduct professional analysis based on them,
ability to present professional opinions both orally and in writing,
ability to address issues related to the professional field, including their management, organization, and financing.
3. General Requirements
Academic Qualifications
an academic degree and/or title in the fields of social sciences and data science, or, in certain cases, a Master鈥檚 degree in the relevant or a related field, including degrees obtained from foreign universities,
at least two scientific and/or methodological publications within the last five years, or practical experience in the field of data science,
participation in at least two conferences and/or workshops within the last five years, or participation in scientific-practical and/or professional events and competitions in the field of data science.
Teaching Experience
at least three years of experience in teaching professional courses and/or conducting training sessions (except for teaching practice by PhD students and courses related to programming language instruction),
participation in local or international training programs and/or professional development courses within the last five years (with the same exceptions as above).
Other Requirements
a teaching portfolio, including online materials for at least one-third of the courses taught,
an average student evaluation score of at least 3.5 (for current teaching staff).
9. Additional information on the programme
The Master鈥檚 program is implemented in cooperation with San Jos茅 State University (USA), the Enterprise Incubator Foundation (EIF), and the Innovative Solutions and Technologies Center (ISTC), with the support of PMI Science. Each year, the best students of the program are given the opportunity to benefit from funding provided by the Enterprise Incubator Foundation (EIF) with the support of PMI Science. Since 2017, a total of 132 students enrolled in the 鈥淒ata Science in Business鈥 Master鈥檚 program have already received financial support.
Within the framework of cooperation with the Innovative Solutions and Technologies Center (ISTC), the entire teaching staff of the program undergoes specialized training, and appropriate software solutions are provided, as well as access to relevant laboratories when necessary for the delivery of courses.
The curriculum of the Master鈥檚 program is developed based on the IBM Academic Initiative, and all educational and software resources are available to both faculty and students through a cloud-based environment. Since September 2020, NVIDIA technologies have been integrated into the 鈥淒ata Science in Business鈥 Master鈥檚 program at the Faculty of Economics and Management of Yerevan State University, enabling a significant enhancement in the quality of higher education delivery.
Cooperation with NVIDIA enables the program not only to 鈥渋mport鈥 knowledge from abroad but also to 鈥渆xport鈥 high-quality expertise. The curriculum of the Master鈥檚 program is synchronized with the Master鈥檚 program in 鈥淒ata Analytics鈥 at San Jos茅 State University, making it more oriented toward programming and applied research.
According to a preliminary agreement with San Jos茅 State University, 3鈥5 of the best second-year Master鈥檚 students may continue their studies in the second year of the 鈥淒ata Analytics鈥 program at San Jos茅 State University and, upon successful completion, receive diplomas from both Yerevan State University and San Jos茅 State University, thereby gaining extensive opportunities to pursue their professional careers in Armenia.
Within the framework of cooperation with the Innovative Solutions and Technologies Center (ISTC), the entire teaching staff of the program undergoes specialized training, and appropriate software solutions are provided, as well as access to relevant laboratories when necessary for the delivery of courses.
The curriculum of the Master鈥檚 program is developed based on the IBM Academic Initiative, and all educational and software resources are available to both faculty and students through a cloud-based environment. Since September 2020, NVIDIA technologies have been integrated into the 鈥淒ata Science in Business鈥 Master鈥檚 program at the Faculty of Economics and Management of Yerevan State University, enabling a significant enhancement in the quality of higher education delivery.
Cooperation with NVIDIA enables the program not only to 鈥渋mport鈥 knowledge from abroad but also to 鈥渆xport鈥 high-quality expertise. The curriculum of the Master鈥檚 program is synchronized with the Master鈥檚 program in 鈥淒ata Analytics鈥 at San Jos茅 State University, making it more oriented toward programming and applied research.
According to a preliminary agreement with San Jos茅 State University, 3鈥5 of the best second-year Master鈥檚 students may continue their studies in the second year of the 鈥淒ata Analytics鈥 program at San Jos茅 State University and, upon successful completion, receive diplomas from both Yerevan State University and San Jos茅 State University, thereby gaining extensive opportunities to pursue their professional careers in Armenia.