Type:
Master
Speciality:
031101.19.7 - Economics
Specialisation:
031101.19.7 - Data Science for Business
Programme academic year:
2025/2026
Mode of study:
Full time
Language of study:
Հայերեն
1. Admission criteria/requirements
Applicants to the Master’s Program in “Data Science in Business” at the Faculty of Economics and Management of Yerevan State University may include graduates holding a Bachelor’s, Master’s, 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’s degree or an equivalent qualification. Admission of applicants with both relevant and non-relevant academic backgrounds (holding a Bachelor’s degree or equivalent qualification) is conducted exclusively on a competitive basis, determined by the results of entrance examinations.
Admission is carried out in accordance with the regulations governing admission to full-time Master’s programs at Yerevan State University.
Applicants to the program must hold a Bachelor’s degree or an equivalent qualification. Admission of applicants with both relevant and non-relevant academic backgrounds (holding a Bachelor’s degree or equivalent qualification) is conducted exclusively on a competitive basis, determined by the results of entrance examinations.
Admission is carried out in accordance with the regulations governing admission to full-time Master’s 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. Educational outcomes of the programme
Upon completion of the course, the student will be able to:
- Professional knowledge and understanding
- Ներկայացնել և համեմատել ժամանակակից հավանականային, օպտիմիզացիոն, վիճակագրական, էկոնոմետրիկ և այլ մեթոդների կիրառությունները։
- Ներկայացնել մասնագիտացված համակարգչային ծրագրերը (R, Java, SQL, Pyton)։
- Վերլուծել ֆինանսական շուկաները և բիզնես գործընթացները՝ օգտագործելով տվյալագիտության մեթոդները, ռեինժինիրինգը, ավտոմատացումը և մոնիթորինգը։
- Practical professional skills
- Իրականացնել մեծ տվյալների հավաքագրում, մշակում և վերլուծություն։
- Կիրառել արհեստական բանականության (AL) և մեքենայական ուսուցման մոդելներ։
- Կիրառել բիզնեսում և հետազոտություններում վիզուալիզացիայի մեթոդներ և դրանց հիման վրա պատրաստել հաշվետվություններ։
- General (transferable) competences
- Օգտագործել մաթեմատիկական և վիճակագրական մեթոդները մասնագիտական և կիրառական խնդիրների լուծման համար։
- Օգտվել տարատեսակ աղբյուրներից` անհրաժեշտ տեղեկատվությունը ստանալու համար, ինչպես նաև համակարգել, վերլուծել և կատարել հետևություններ ստացված տվյալներով։
- Իրականացնել միջգիտակարգային հետազոտություններ, որոնք պահանջում են գիտության, տեխնիկայի և տնտեսության տարբեր բնագավառների տվյալների հավաքագրում և համադրում, ինչպես նաև խորը վերլուծություն։
- Պատրաստել զեկուցումներ, ներկայացնել հետազոտությունների արդյունքները և վարել գիտական բանավեճեր:
4. Assessment methods
At Yerevan State University, a multi-component system of continuous assessment and evaluation of students’ 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 предусмотрed by the program (independent work),
4. assessment of independent and/or group research work предусмотрed 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) предусмотрed by the curriculum, the form of instruction, teaching methods, and the importance of the course in shaping students’ 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 предусмотрed by the program (independent work),
4. assessment of independent and/or group research work предусмотрed 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) предусмотрed by the curriculum, the form of instruction, teaching methods, and the importance of the course in shaping students’ 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 future career opportunities
The potential employment opportunities and career paths of the program graduates are determined by students’ 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 or programme benchmarks used for 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’s Program in “Data Analytics” at San José State University (USA).
* The Framework of Qualifications for the European Higher Education Area (2010).
* The Master’s Program in “Data Analytics” at San José State University (USA).
8. Requirements for the academic staff
The teaching staff of the “Data Science in Business” 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 “Data Science in Business” Master’s 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’s 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’s 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’s 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’s 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 about the programme
The Master’s program is implemented in cooperation with San José 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 “Data Science in Business” Master’s 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’s 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 “Data Science in Business” Master’s 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 “import” knowledge from abroad but also to “export” high-quality expertise. The curriculum of the Master’s program is synchronized with the Master’s program in “Data Analytics” at San José State University, making it more oriented toward programming and applied research.
According to a preliminary agreement with San José State University, 3–5 of the best second-year Master’s students may continue their studies in the second year of the “Data Analytics” program at San José State University and, upon successful completion, receive diplomas from both Yerevan State University and San José 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’s 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 “Data Science in Business” Master’s 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 “import” knowledge from abroad but also to “export” high-quality expertise. The curriculum of the Master’s program is synchronized with the Master’s program in “Data Analytics” at San José State University, making it more oriented toward programming and applied research.
According to a preliminary agreement with San José State University, 3–5 of the best second-year Master’s students may continue their studies in the second year of the “Data Analytics” program at San José State University and, upon successful completion, receive diplomas from both Yerevan State University and San José State University, thereby gaining extensive opportunities to pursue their professional careers in Armenia.
