Type:
Master
Speciality:
031101.19.7 - Economics
Specialisation:
031101.19.7 - Data Science for Business
Qualification awarded:
031101.19.7 - Economics
Programme academic year:
2024/2025
Mode of study:
Full time
Language of study:
Հայերեն
1. Admission criteria/requirements
ADMISSION CRITERIA/REQUIREMENTS
All graduates with Bachelor's, Master's degrees, or specialists with diplomas from RA state and non-state universities accredited by specialties have the opportunity to apply for program admission.
Applicants must have Bachelor's degrees or equivalent qualifications, and the admission is carried out exclusively based on the results of the entrance exams.
The admission is carried out according to the admission regulations of the YSU’s Master's degree programs.
All graduates with Bachelor's, Master's degrees, or specialists with diplomas from RA state and non-state universities accredited by specialties have the opportunity to apply for program admission.
Applicants must have Bachelor's degrees or equivalent qualifications, and the admission is carried out exclusively based on the results of the entrance exams.
The admission is carried out according to the admission regulations of the YSU’s Master's degree programs.
2. Programme Objectives
AIM OF PROGRAM
The goal of the program is to train competitive specialists who meet international education standards, representing three fields at once: mathematics, programming, and business analysis.
At the same time, the program is focused on practical applications of this knowledge in business processes and is not a tool for providing purely theoretical, abstract knowledge. Such specialists will be able to:
·become a part of the data revolution taking place in the world by connecting theoretical knowledge in data science with business requirements, using Python, SQ, R, and Java programming languages.
·effectively apply economic-mathematical methods and models,
·make business decisions using data science, machine learning, and artificial intelligence methods
·analyze and interpret the results obtained by their application,
·achieve analytical and research abilities, allowing them to engage in further scientific activities.
The goal of the program is to train competitive specialists who meet international education standards, representing three fields at once: mathematics, programming, and business analysis.
At the same time, the program is focused on practical applications of this knowledge in business processes and is not a tool for providing purely theoretical, abstract knowledge. Such specialists will be able to:
·become a part of the data revolution taking place in the world by connecting theoretical knowledge in data science with business requirements, using Python, SQ, R, and Java programming languages.
·effectively apply economic-mathematical methods and models,
·make business decisions using data science, machine learning, and artificial intelligence methods
·analyze and interpret the results obtained by their application,
·achieve analytical and research abilities, allowing them to engage in further scientific activities.
3. Educational outcomes of the programme
Upon completion of the course, the student will be able to:
- describe modern data analysis methods and models
- present the most widely used directions in the application of modern probabilistic, optimization, statistical, econometric and other methods
- present the most widespread specialized computer programs, as well as the main directions of their application for economic analysis
- interpret the main concepts of micro-macroeconomics, the problems of global and local financial markets, using data science methods, present the main elements of business process projects, reengineering, automation, and monitoring
- introduce applied statistics and data mining methods used for individual organization and market analysis and forecasting
- make calculations and economic forecasts using modern probabilistic, optimization, statistical, econometric, and other methods
- use appropriate computer packages: R, Python, SQ, and Java to solve various professional problems
- apply cloud technologies in business process modeling and documentation processes
- carry out collection, classification, and clustering of data collected in different fields
- apply Python and R programming packages to solve various business problems
- present the ways of building software systems with a modeling orientation
- describe modern data analysis methods and models
- use mathematical and statistical methods to solve professional and applied problems
- using programming tools to build effective algorithms to solve various problems related to this field
- learn to use different sources to get the necessary information
- process and analyze the received data
- specialize in various fields of science, technology, and economics, where deep data collection and analysis skills are required, as well as basic knowledge of applied statistics
4. Assessment methods
ASSESSMENT
The assessment includes the following components:
1. assessment of mastering the sub-sections of the course during the semester (2 mid-term exams),
2. mid-term check-ups of individual topics of the course during the semester,
3. verification and assessment of the implementation and assimilation of individual tasks provided by the program during the semester (individual work),
4. evaluation of the performance of independent and/or group research provided by the program during the semester (research replaces one of the mid-term exams),
5. assessment of participation in the course,
6. final assessment of the entire course in the exam period, which implies an assessment of the level of achievement of educational outcomes defined for the course.
According to the evaluation form, the courses are divided into 4 groups:
1. with the final assessment,
2. without final assessment,
3. without evaluation of mid-term exams,
4. check-up.
The assessment includes the following components:
1. assessment of mastering the sub-sections of the course during the semester (2 mid-term exams),
2. mid-term check-ups of individual topics of the course during the semester,
3. verification and assessment of the implementation and assimilation of individual tasks provided by the program during the semester (individual work),
4. evaluation of the performance of independent and/or group research provided by the program during the semester (research replaces one of the mid-term exams),
5. assessment of participation in the course,
6. final assessment of the entire course in the exam period, which implies an assessment of the level of achievement of educational outcomes defined for the course.
According to the evaluation form, the courses are divided into 4 groups:
1. with the final assessment,
2. without final assessment,
3. without evaluation of mid-term exams,
4. check-up.
5. Graduates future career opportunities
CAREER OPPORTUNITIES
Graduates of the program are competitive professionals in today's job market. With their up-to-date knowledge, they have become an important part of the data revolution, taking place in the world, connecting theoretical knowledge in data science with business requirements, using Python, R, Java, and SQ programming languages, easily making business decisions using data science, machine learning, and artificial intelligence methods.
Graduates of the educational program today work in leading IT organizations: Picsart, SmartClickAI, WorldQuant, Krisp, Plat.ai, Service Titan, WebbFontaine, PMI Science, Cognaize, Synopsys Armenia, NVIDIA, DataMotus, Sololearn, DataArt, as well as in RA Government, and international organizations.
Graduates of the program are competitive professionals in today's job market. With their up-to-date knowledge, they have become an important part of the data revolution, taking place in the world, connecting theoretical knowledge in data science with business requirements, using Python, R, Java, and SQ programming languages, easily making business decisions using data science, machine learning, and artificial intelligence methods.
Graduates of the educational program today work in leading IT organizations: Picsart, SmartClickAI, WorldQuant, Krisp, Plat.ai, Service Titan, WebbFontaine, PMI Science, Cognaize, Synopsys Armenia, NVIDIA, DataMotus, Sololearn, DataArt, as well as in RA Government, and international organizations.
6. Resources and forms to support learning
RESOURCES AND ASSISTANCE FORMS TO SUPPORT LEARNING
The following auxiliary resources are used in the learning process:
·laboratories equipped with modern devices and software,
·electronic resources,
·istc-ysu.ibmonthehub.com cloud system.
The following auxiliary resources are used in the learning process:
·laboratories equipped with modern devices and software,
·electronic resources,
·istc-ysu.ibmonthehub.com cloud system.
7. Educational standards or programme benchmarks used for programme development
EDUCATIONAL STANDARDS OR PROGRAM BENCHMARKS USED FOR PROGRAM DEVELOPMENT
·State general standard of higher professional education
·RA national framework of educational qualifications
·The framework of qualifications for the European Higher Education Area, 2010.
·State general standard of higher professional education
·RA national framework of educational qualifications
·The framework of qualifications for the European Higher Education Area, 2010.
8. Requirements for the academic staff
REQUIREMENTS FOR THE ACADEMIC STAFF
The teaching staff of the "Data Science in Business" educational program is constantly replenished with young professors of the new generation who work in the RA IT sector and at the same time are part of our team. Five of the instructors in the program are PhD students in Data Science and Artificial Intelligence. Many of them are graduates of the Data Science in Business Master's program and PMI scholarship holders. This process is very important as it ensures the stability of the program, young professors constantly apply all the news and interesting ideas in the RA IT sector in their courses, that is, thanks to the Master's program, the link between education and the labor market is actively developing. The young teaching staff of the program consists of young but experienced academic and IT specialists.
1. General Abilities.
Teaching/Pedagogical
·the ability to make a coursework program (calendar plan),
·knowledge of interactive teaching methods, and ability to use active learning techniques.
Research
·ability to work with various scientific sources, as well as to use Internet information resources,
·ability to conduct sociological and marketing research,
·ability to lead a research group.
Communication
·ability to communicate orally with the audience,
·ability to present research results in writing,
·knowledge of at least one foreign language (at least B1 level of English).
ICT application
·basic computer skills (fluent command of MS Word, MS Excel, MS PowerPoint package), command of Python/R, Java, SQ software packages,
·ability to use modern social platforms,
·skills to prepare and present presentations (pptx, prezi, canva, etc.).
Other abilities
·knowledge of professional ethics and legal norms regulating public relations,
·ability to assess the necessary resources and implement projects effectively,
·ability to plan and manage time.
2. Professional Abilities
·ability to transfer knowledge and skills presented in the course description to students,
·ability to conduct professional research,
·ability to develop and present economic strategies, plan and implement projects,
·ability to work with the most popular professional computer packages,
·ability to use various reports and perform professional analysis based on them,
·ability to present oral and written opinions on issues related to the professional field,
·ability to present issues related to the issues of the professional sphere, their management, organization, and financing.
3. General requirements
Academic degree
·degree in social sciences and data sciences, or, in some cases, a Master's degree in a given or related specialty, including at foreign universities,
·availability of at least 2 scientific and/or methodological publications in the last 5 years or experience of practical work in data science,
·in the last 5 years, at least 2 participations in conferences and/or workshops or scientific-practical and/or business conferences and competitions in data science.
Pedagogical experience
·at least 3 years of experience in teaching professional courses and/or conducting trainings (except internship),
·participation in local or international trainings and/or professional qualification improvement courses during the last 5 years (except internship),
Other requirements
·teaching portfolio: availability of online materials for at least 1/3 of the taught subjects,
·average of grades obtained by the results of the student survey: at least 3.5 (for teaching professors).
The teaching staff of the "Data Science in Business" educational program is constantly replenished with young professors of the new generation who work in the RA IT sector and at the same time are part of our team. Five of the instructors in the program are PhD students in Data Science and Artificial Intelligence. Many of them are graduates of the Data Science in Business Master's program and PMI scholarship holders. This process is very important as it ensures the stability of the program, young professors constantly apply all the news and interesting ideas in the RA IT sector in their courses, that is, thanks to the Master's program, the link between education and the labor market is actively developing. The young teaching staff of the program consists of young but experienced academic and IT specialists.
1. General Abilities.
Teaching/Pedagogical
·the ability to make a coursework program (calendar plan),
·knowledge of interactive teaching methods, and ability to use active learning techniques.
Research
·ability to work with various scientific sources, as well as to use Internet information resources,
·ability to conduct sociological and marketing research,
·ability to lead a research group.
Communication
·ability to communicate orally with the audience,
·ability to present research results in writing,
·knowledge of at least one foreign language (at least B1 level of English).
ICT application
·basic computer skills (fluent command of MS Word, MS Excel, MS PowerPoint package), command of Python/R, Java, SQ software packages,
·ability to use modern social platforms,
·skills to prepare and present presentations (pptx, prezi, canva, etc.).
Other abilities
·knowledge of professional ethics and legal norms regulating public relations,
·ability to assess the necessary resources and implement projects effectively,
·ability to plan and manage time.
2. Professional Abilities
·ability to transfer knowledge and skills presented in the course description to students,
·ability to conduct professional research,
·ability to develop and present economic strategies, plan and implement projects,
·ability to work with the most popular professional computer packages,
·ability to use various reports and perform professional analysis based on them,
·ability to present oral and written opinions on issues related to the professional field,
·ability to present issues related to the issues of the professional sphere, their management, organization, and financing.
3. General requirements
Academic degree
·degree in social sciences and data sciences, or, in some cases, a Master's degree in a given or related specialty, including at foreign universities,
·availability of at least 2 scientific and/or methodological publications in the last 5 years or experience of practical work in data science,
·in the last 5 years, at least 2 participations in conferences and/or workshops or scientific-practical and/or business conferences and competitions in data science.
Pedagogical experience
·at least 3 years of experience in teaching professional courses and/or conducting trainings (except internship),
·participation in local or international trainings and/or professional qualification improvement courses during the last 5 years (except internship),
Other requirements
·teaching portfolio: availability of online materials for at least 1/3 of the taught subjects,
·average of grades obtained by the results of the student survey: at least 3.5 (for teaching professors).
9. Additional information about the programme
ADDITIONAL INFORMATION ABOUT THE PROGRAM
The Master's program is implemented in collaboration with San Jose State University, Enterprise Incubator Fund (EIF), and Innovation Solutions and Technologies Center (ISTC) with the support of PMI Science.
The best students of the program each year have the opportunity to take advantage of the funding opportunity provided by the Enterprise Incubator Fund (EIF) with the support of PMI Science. Since 2017, 103 students have already been allocated funding.
Within the framework of cooperation with the Center for Innovative Solutions and Technologies, all the teaching staff of the program undergo special training courses, where appropriate software solutions, and laboratories to carry out the courses are provided.
Courses are based on the IBM Academic Initiative, and all educational and software resources are available in the cloud for faculty and students.
Since September 2020, Nvidia technologies have been used in the Master's program "Data Science in Business" of the YSU Faculty of Economics and Management, which provides an opportunity to raise the quality of higher education teaching to a new level.
The cooperation with "Nvidia" enables YSU's "Data Science in Business" Master's program not only to "import knowledge" from abroad but also to "export".
The curriculum program is synchronized with the Master's program "Data Analytics" of San Jose State University, USA; as a result, the program has become more oriented toward programming and applied research. According to the preliminary agreement reached with San Jose State University, USA, the 3-5 best Master's students can continue their studies in the 2nd year of the "Data Analysis" program of SJSU and upon successful completion, receive a diploma from both YSU and SJSU, getting great opportunities to expand their activities in Armenia.
The Master's program is implemented in collaboration with San Jose State University, Enterprise Incubator Fund (EIF), and Innovation Solutions and Technologies Center (ISTC) with the support of PMI Science.
The best students of the program each year have the opportunity to take advantage of the funding opportunity provided by the Enterprise Incubator Fund (EIF) with the support of PMI Science. Since 2017, 103 students have already been allocated funding.
Within the framework of cooperation with the Center for Innovative Solutions and Technologies, all the teaching staff of the program undergo special training courses, where appropriate software solutions, and laboratories to carry out the courses are provided.
Courses are based on the IBM Academic Initiative, and all educational and software resources are available in the cloud for faculty and students.
Since September 2020, Nvidia technologies have been used in the Master's program "Data Science in Business" of the YSU Faculty of Economics and Management, which provides an opportunity to raise the quality of higher education teaching to a new level.
The cooperation with "Nvidia" enables YSU's "Data Science in Business" Master's program not only to "import knowledge" from abroad but also to "export".
The curriculum program is synchronized with the Master's program "Data Analytics" of San Jose State University, USA; as a result, the program has become more oriented toward programming and applied research. According to the preliminary agreement reached with San Jose State University, USA, the 3-5 best Master's students can continue their studies in the 2nd year of the "Data Analysis" program of SJSU and upon successful completion, receive a diploma from both YSU and SJSU, getting great opportunities to expand their activities in Armenia.
