Type:
Master
Speciality:
056201.04.7 - Statistics
Specialisation:
056201.04.7 - Applied statistics and data science
Programme academic year:
2024/2025
Mode of study:
Full time
Language of study:
Հայերեն
1. Admission criteria/requirements
"Applied Statistics and Data Science" program is organized according to the YSU master's degree admission regulations.
Any person with bachelor's degree from RA state or accredited non-state higher educational institutions can be admitted to the master's program in a competitive manner. The admission of graduates of foreign universities to the master's degree program is based on the interview and the opinion of the scientific council of the Faculty of Mathematics and Mechanics.
Any person with bachelor's degree from RA state or accredited non-state higher educational institutions can be admitted to the master's program in a competitive manner. The admission of graduates of foreign universities to the master's degree program is based on the interview and the opinion of the scientific council of the Faculty of Mathematics and Mechanics.
2. Programme Objectives
· to provide students with fundamental knowledge in the fields of data science and statistics by introducing them to statistics and its applications, to the mathematics of machine learning and related fields, to the methods for working with big data,
· to provide students with knowledge about professional computer programs (Python and R programming languages), teach them skills to solve practical problems,
· to provide students with practical knowledge, enabling them to be competitive in the professional field of the labor market,
· to develop analytical and research abilities of students, giving them the opportunity to engage in further scientific activities.
· to provide students with knowledge about professional computer programs (Python and R programming languages), teach them skills to solve practical problems,
· to provide students with practical knowledge, enabling them to be competitive in the professional field of the labor market,
· to develop analytical and research abilities of students, giving them the opportunity to engage in further scientific activities.
3. Educational outcomes of the programme
Upon completion of the course, the student will be able to:
- Demonstrate a comprehensive understanding of the fundamentals of Probability Theory and Statistics.
- Exhibit proficiency in constructing computer programs with a modeling orientation.
- Apply probabilistic and statistical methods effectively in the natural sciences.
- Justify the theoretical foundations of the field of specialization.
- Articulate recent methods and models for Data Analysis.
- Apply mathematical and statistical methods and tools proficiently to solve both applied and theoretical problems.
- Grasp and adapt to new methods emerging daily in response to the rapid growth in the field.
- Process and analyze datasets, formulate statistical models for challenges in diverse areas, and make informed decisions based on these models.
- Develop efficient algorithms using programming tools to address a variety of field-specific problems.
- Implement algorithms using the Python programming language.
- Design databases and other resources efficiently to support the needs of the field.
- Utilize various sources to acquire necessary information effectively.
- Systematically organize and analyze obtained information, draw meaningful conclusions.
- Process acquired data, make informed decisions, actively participate in discussions and debates, and articulate and present obtained results.
- Specialize in different fields of science, technology, or economy, where analytical skills and applied statistical knowledge are essential.
4. Assessment methods
The assessment includes the following components : 1. Assessment of mastery of appropriate sections of the course/module during the semester (2 midterm exams), 2. Quizzes on individual topics of the course/module during the semester, 3. Evaluation of the homework/individual tasks during the semester (individual work), 4. Evaluation of individual and/or group projects during the semester (projects that replace one of the midterm exams), 5. Evaluation of attendance/participation in the course (participation), 6. Final assessment of the entire course/module during the exam period, which implies an assessment of the level of achievement of the educational outcomes set for the course. According to the form of evaluation, the courses are divided into 4 groups: - with a final exam - without a final exam - without midterm exams - pass/no pass.
5. Graduates future career opportunities
The main target employers for program graduates are IT sector companies, such as Picsart, Krisp, WebbFontain, Service Titan and others , and all those institutions (state institutions, banks, commercial organizations, etc.) that have data analysis departments.
Graduates can apply to positions for statistical analyst, machine learning researcher and engineer, data scientist, data analyst, and many others. The program cooperates with a number of leading organizations in the field in terms of students’ internship and research work, and further employment possibilities.
The knowledge acquired by the graduates of the program gives them the opportunity to continue their studies in RA and foreign universities, and later to work in academic research institutions.
Graduates can apply to positions for statistical analyst, machine learning researcher and engineer, data scientist, data analyst, and many others. The program cooperates with a number of leading organizations in the field in terms of students’ internship and research work, and further employment possibilities.
The knowledge acquired by the graduates of the program gives them the opportunity to continue their studies in RA and foreign universities, and later to work in academic research institutions.
6. Resources and forms to support learning
The program is supported by a modern equipped computer classroom. During the study, students will be provided with all necessary materials: literature, articles, necessary computer programs, and other required electronic resources.
7. Educational standards or programme benchmarks used for programme development
1. RA National Framework of Qualifications, approved by RA Government Resolution N 714-N of July 7, 2016.
2. "Mathematics" sectoral framework of qualifications, 2022.
3. European Framework of Qualifications, 2008.
The program has been developed taking into account similar master's programs of several top universities from Europe and America, including MIT, Stanford, Edinburgh, Oxford, Lausanne Polytechnic, Zurich University of Technology, Moscow Higher School of Economics, combining them with the requirements/peculiarities of our industry and academy in our country, and also with the availability of teaching staff.
2. "Mathematics" sectoral framework of qualifications, 2022.
3. European Framework of Qualifications, 2008.
The program has been developed taking into account similar master's programs of several top universities from Europe and America, including MIT, Stanford, Edinburgh, Oxford, Lausanne Polytechnic, Zurich University of Technology, Moscow Higher School of Economics, combining them with the requirements/peculiarities of our industry and academy in our country, and also with the availability of teaching staff.
8. Requirements for the academic staff
1. General abilities
Teaching/Pedagogical
· Ability to make a syllabus (calendar plan) ,
· Knowledge of interactive teaching methods, ability to use active learning techniques.
Research
· Ability to work with various scientific sources, as well as to use Internet resources,
· Ability to lead a student research group.
Communication
· Ability to communicate orally with the audience,
· Ability to present research results in a written form,
· Ability to communicate in foreign language in the professional field
ICT application
· Basic computer skills (fluency in MS Office package: Word, Excel, PowerPoint),
· Skills in making and presenting slideshows.
Other abilities
· Compliance with the norms of professional ethics,
· Ability to estimate necessary resources and implement projects effectively;
· Ability to plan and manage time resources.
2. Professional abilities
· Familiarity with the professional subjects of the Master's program
· Thorough knowledge of the taught course
· Mastery of key concepts of modules related to the taught course
· Ability to include a research component into the course
· Ability to supervise a master's thesis
3. General requirements
Academic degree
· Scientific degree or master's degree in statistics or related fields;
· at least 2 scientific and/or methodological publications in the last 5 years ,
· participation in conferences and/or work conferences in the last 5 years.
Pedagogical experience
· participation in local or international training and/or professional qualification improvement courses during the last 5 years.
Other requirements
· Follow the requirements of the YSU Code of Conduct,
· Average of grades obtained from the student’s surveys: at least 4.0 (for full time faculty).
Teaching/Pedagogical
· Ability to make a syllabus (calendar plan) ,
· Knowledge of interactive teaching methods, ability to use active learning techniques.
Research
· Ability to work with various scientific sources, as well as to use Internet resources,
· Ability to lead a student research group.
Communication
· Ability to communicate orally with the audience,
· Ability to present research results in a written form,
· Ability to communicate in foreign language in the professional field
ICT application
· Basic computer skills (fluency in MS Office package: Word, Excel, PowerPoint),
· Skills in making and presenting slideshows.
Other abilities
· Compliance with the norms of professional ethics,
· Ability to estimate necessary resources and implement projects effectively;
· Ability to plan and manage time resources.
2. Professional abilities
· Familiarity with the professional subjects of the Master's program
· Thorough knowledge of the taught course
· Mastery of key concepts of modules related to the taught course
· Ability to include a research component into the course
· Ability to supervise a master's thesis
3. General requirements
Academic degree
· Scientific degree or master's degree in statistics or related fields;
· at least 2 scientific and/or methodological publications in the last 5 years ,
· participation in conferences and/or work conferences in the last 5 years.
Pedagogical experience
· participation in local or international training and/or professional qualification improvement courses during the last 5 years.
Other requirements
· Follow the requirements of the YSU Code of Conduct,
· Average of grades obtained from the student’s surveys: at least 4.0 (for full time faculty).
