PhD program
Data Science

Data Science

QUALIFICATION

  • Scientific and pedagogical direction - Doctor of Philosophy (PhD)

MODEL OF GRADUATING STUDENT

1.To identify tools and methodologies that require significant computational resources for statistical analysis and visualization of complex data and effectively apply statistical computing methods to real datasets.

Program passport

Speciality Name
Data Science
Speciality Code
8D06107
Faculty
Information technology

disciplines

Academic Writing
  • Number of credits - 2
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The course introduces doctoral students to the main features of the scientific style of speech and writing, building a logical presentation of the text, critical assessment of information sources, and forming the skills of creating academic and scientific-pedagogical texts, publications, and presentations.

Advanced Topics in Mathematical Statistics
  • Number of credits - 5
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The purpose of the discipline: is to develop the ability to conduct research in the field of multivariate statistics and Bayesian modeling, apply statistical machine learning methods to data analysis. Learning outcomes: 1. Conduct research in the field of multivariate statistics and data visualization. 2. Analyze at the conceptual level multivariate methods of statistical analysis. 3. Perform the modeling and calculations necessary to perform advanced data analysis from a Bayesian perspective. 4. Explore the statistical aspects of machine learning and automated thinking through the use of (sample) data. 5. Analyze the performance of statistical machine learning algorithms.

Integrative Data Science
  • Number of credits - 5
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The purpose of the discipline: is to develop the ability to integrate and analyze various data sources to extract meaningful information and make informed decisions. Learning outcomes: 1. Integrate and analyze data from various fields. 2. Analyze and design the data life cycle from acquisition to interpretation and action. 3. Apply statistical modeling and machine learning visualization techniques to solve complex data science problems. 4. Effectively visualize and communicate information. 5. Include ethical and privacy considerations in data science integration projects.

PhD Thesis Writing and Defence
  • Number of credits - 12
  • Type of control - Докторская диссертация
  • Description - The main purpose of "PhD thesis writing and defence": of a doctoral dissertation is the formation of the doctoral students' ability to disclose the content of research work for the defense of the thesis. During the study of course, doctoral student's should be competent in: 1. to substantiate the content of new scientifically grounded theoretical and experimental results that allow to solve a theoretical or applied problem or are a major achievement in the development of specific scientific directions; 2. explain the assessment of the completeness of the solutions to the tasks assigned, according to the specifics of the professional sphere of activity; 3. they can analyze alternative solutions for solving research and practical problems and assess the prospects for implementing these options; 4. apply the skills of writing scientific texts and presenting them in the form of scientific publications and presentations. 5. to plan and structure the scientific search, clearly highlight the research problem, develop a plan / program and methods for its study, formalize, in accordance with the requirements of the State Educational Establishment, the scientific and qualification work in the form of a thesis for a scientific degree Doctor of Doctor of Philosophy (PhD) on specialty «8D07502 – Standardization and certification (by industry)». During the study of the discipline doctoral student will learn following aspects: Registration of documents for presentation of the thesis for defense. Information card of the dissertation and registration-registration card (in the format Visio 2003). Extract from the minutes of the meeting of the institution, in which the preliminary defense of the thesis took place. Cover letter to the Higher Attestation Commission. Expert conclusion on the possibility of publishing the author's abstract. Expert opinion on the possibility of publishing a dissertation. Minutes of the meeting of the counting commission. Bulletin for voting. A shorthand record of the meeting of the dissertational council. List of scientific papers. Response of the official opponent. A review of the leading organization. The recall of the scientific adviser.

Scientific Research Methods
  • Number of credits - 3
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - Knowledge of this discipline is necessary for the study and analysis of scientific research, statement of the scientific and pedagogical problem, theoretical and experimental research, the selection of the necessary research methods and tools that allow a logical generalization of the facts collected, develop concepts and judgments, make conclusions and theoretical conclusions with the purpose of developing recommendations and research results.

Data for 2021-2024 years

disciplines

Advanced Probability Theory
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The purpose of the discipline: is to develop the ability to apply various aspects of probability theory, from basic theorems to martingale theory in discrete time, to solve data science problems. Learning outcomes: 1. State the main ideas underlying the theory of probability. 2. Practice basic concepts of probability such as: distribution, expectation, variance, independence, conditional probability. 3. Apply the theory of probability to build mathematical models and solve statistical problems. 4. Determine the types of practical problems that can be solved using probabilistic methods, and the ability to use the knowledge gained to solve them. 5. Create mathematical tools for data analysis based on probabilistic methods.

Big data access and management
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The purpose of the discipline: is to develop the ability to efficiently extract, store, process and analyze big data to support data-driven decision making. Learning outcomes: 1. Apply principles, methods and tools related to accessing and managing large scale datasets. 2. Compare different approaches to handling volumes, diversity, and data rates. 3. Efficiently extract, store, process and analyze big data to gain valuable insights and make data-driven decisions. 4. Work with big data using various methods of storage, processing, analysis, visualization and management. 5. Anticipate and bear professional responsibility for the ethical consequences of obtaining, processing and analyzing data.

Computational statistics
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The objective of the course is to educate students on the application of computational methods and techniques for solving statistical problems. It aims to develop students' skills and understanding necessary for utilizing computer algorithms and programs in statistical data analysis. Students learn to use R and SAS tools.

Machine learning theory
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The purpose of the discipline: is to develop the ability to develop mathematical tools for the design and theoretical analysis of machine learning methods. Learning outcomes: 1. Formalize machine learning tasks in statistical and game-theoretic conditions. 2. Explore the statistical complexity of machine learning problems using the basic concepts of complexity. 3. Analyze the statistical efficiency of learning algorithms. 4. Develop machine learning strategies using proper regularization. 5. Apply an engineering approach to create solutions that meet specific needs, taking into account global, cultural, social, environmental and economic factors.

Data for 2021-2024 years

INTERNSHIPS

Pedagogical
  • Type of control - Защита практики
  • Description - Formation of practical, educational-methodical skills of conducting lectures, seminars, creatively apply scientific, theoretical knowledge, practical skills in teaching activities, conduct training sessions in the disciplines of the specialty; own modern professional techniques, methods of training, use in practice the latest theoretical, methodological advances, make educational, methodological documentation.

Research
  • Type of control - Защита практики
  • Description - The purpose of the practice: gaining experience in the study of an actual scientific problem, expand the professional knowledge gained in the learning process, and developing practical skills for conducting independent scientific work. The practice is aimed at developing the skills of research, analysis and application of economic knowledge.

Data for 2021-2024 years