PhD courses
Speciality Code:
8D06107
Speciality Name:
Data Science
Faculty:
Information technology
Qualification:
- Scientific and pedagogical direction - Doctor of Philosophy (PhD)
- Model of graduating student
- Mandatory disciplines
- Elective disciplines
- Professional
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Pedagogical
- Type of control: Защита практики
- Description: Aim оf discipline: formation of the ability to carry out educational activities in universities, to design the educational process and conduct certain types of training sessions using innovative educational technologies.
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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.