Magistracy
Speciality Name:
Data Science
Faculty:
Information technology
Qualification:
- Scientific and pedagogical direction - Master of Engineering Sciences
- Model of graduating student
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Mandatory
disciplines
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Elective
disciplines
- Professional
1. Apply methods for collecting, preprocessing, visualizing data from heterogeneous sources to get an idea of the subject area under study, identify patterns and support decision-making based on data analysis.
2. Apply the methods of statistical analysis, linear algebra, optimization, mathematical analysis and computational tools necessary to effectively extract useful information from structured and unstructured data sets of any size.
3. Develop data processing applications, implement basic computational algorithms for data analysis, evaluate the computational complexity of algorithms, design and use relational and non-relational databases, and carry out practical data analysis projects in collaboration with industry partners.
4. Organize, visualize and analyze large complex datasets using descriptive statistics methods, develop big data management applications in various fields, develop, install and configure cloud computing applications, and apply virtual machine computing environments for scalable data processing.
5. Explore various use cases for blockchain technology in various industries, design and develop decentralized applications based on blockchain technology, take into account ethical issues, analyze the potential consequences of using blockchain for society and the economy.
6. Develop and optimize machine learning models and methods for data analysis and visualization in solving applied problems, apply deep learning models in scientific research, innovative projects and real applications.
7. Analyze data privacy issues, comply with ethical standards, privacy principles and data security measures related to the collection, analysis and use of data in various contexts, apply technical mechanisms to ensure data security and confidentiality.
8. Conduct an in-depth analysis of the research area to select suitable data analysis methods, use knowledge and skills to continue learning and adapt to new data processing technologies, develop critical thinking about data and data analysis-based decisions, lead a research team.
9. Independently conduct scientific research, understand current research issues, analyze and critically relate to various sources of information, use them to structure and formulate reasoning, conduct scientific and pedagogical activities, implement research results in practical pedagogical activities.
10. Apply methods and tools for data analysis in various multidisciplinary areas, present research results in various forms in national scientific publications, at conferences, taking into account the specifics of the audience, be tolerant, work effectively in a team when searching for and solving research problems.
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Organization and Planning of Scientific Research (in English)
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The discipline aims to form the ability to apply practical skills in organising and planning scientific research. The discipline studies forms and methods of planning, organisation, and design of scientific articles and dissertations, as well as summarising scientific research results in presentations, speeches, projects, and articles.
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Foreign Language (professional)
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose is to acquire and improve competencies by international standards of foreign language education and to communicate in an intercultural, professional, and scientific environment. A master's student must integrate new information, understand the organization of languages, interact in society, and defend his point of view.
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History and Philosophy of Science
- Number of credits: 3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The course forms knowledge about the history and theory of science; on the laws of the development of science and the structure of scientific knowledge; about science as a profession and social institution; оn the methods of conducting scientific research; the role of science in the development of society.
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Mathematical Methods in Data Science
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The aim of the discipline is to develop the ability to apply mathematical methods, including probability, linear algebra, calculus and optimization, ordinary differential equations and partial differential equations, in data science.
Course content: Linear least squares and singular value decomposition. Spectral Graph Theory and Applications. Optimization in machine learning. Optimal conditions. Convex. Gradient Descent: Convergence Analysis. back propagation. Stochastic gradient descent for logistic regression. Probabilistic models: key concepts and examples.
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Pedagogy of Higher education
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose is the formation of the ability of pedagogical activity through the knowledge of higher education didactics, theories of upbringing and education management, analysis, and self-assessment of teaching activities. The course covers the educational activity design of specialists, Bologna process implementation, acquiring a lecturer, and curatorial skills by TLA-strategies.
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Programming for Data Science
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The aim of this discipline is to develop the ability to create applications for data processing and manage software development projects.
Course content: Software development life cycle. Preprocessing of raw data. Key stages of creating a data analysis pipeline. Data processing. Methods of knowledge extraction from data. Data visualization. Interacting with machine learning frameworks. Developing software for data analytics. Parallelism in Python. Using Git and GitHub.
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Database design and management
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the ability to design and use relational and non-relational databases, build complex reports and conduct deep data analysis through queries.
Within the framework of the discipline the following aspects are considered: Relational data model. Analysis and design, implementation of a relational database using SQL. Designing NoSQL databases. Prepare, explore and extract data from a NoSQL database.
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Psychology of Management
- Number of credits: 3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The course reveals the subject, the basic principles of management psychology, personality in managerial interactions, personal behavior management, psychology of managing group phenomena and processes, psychological characteristics of the leader's personality, individual management style, psychology of influence in management activities, conflict management.
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Ethics, privacy and data security
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to comply with ethical standards, confidentiality principles and data security measures related to the collection, analysis and use of data in various contexts Content of the discipline: Ethical standards in handling data. Data protection rules. Ways to keep data confidential. Encryption/decryption, message authentication, data integrity. Threat models for data-driven applications. Public Key Cryptography for Data Protection
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Applied Machine Learning
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to apply machine learning algorithms and methods for analyzing and visualizing data in solving applied problems.
Within the framework of the discipline, the following aspects are considered: Data preprocessing and feature engineering. Methods for Feature Selection and Dimension Reduction. Supervised learning algorithms. Learning algorithms without a teacher. Model evaluation and validation. Evaluation metrics for classification, regression and clustering. Cross validation and hyperparameter tuning. Advanced machine learning methods. Practical applications and case studies
Data for 2021-2024 years
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Blockchain systems architecture
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the discipline is to develop the ability to understand the basics of the blockchain architecture and its components, to apply decentralized networks and consensus mechanisms in the deployment and implementation of the blockchain. Contents: Basic concepts of the blockchain. Types of blockchain systems. Advantages and disadvantages. Architectural components of the blockchain. Protocols and consensus algorithms. The principles of operation of the Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS) algorithms and others. Scalability and performance. blockchain systems. Interoperability and standards. blockchain systems. Standards and protocols ERC-20, ERC-721, Hyperledger Fabric and others
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Smart contracts architecture
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the course is to teach undergraduates the basics of smart contract architecture and provide them with an understanding of the principles and methods for designing efficient and reliable smart contracts.
The course covers the following aspects:
Introduction to smart contracts. The concept of smart contracts and their role in blockchain technology. Basic principles of smart contracts, their structure and main functions. Programming languages for smart contracts. Smart contract architecture. Design principles and architecture of smart contracts. Modularity, inheritance, interfaces and other aspects for the development of flexible and efficient smart contracts. State management and data storage. Data structure and basic operations for storing and modifying data in smart contracts.
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Blockchain Bisiness Models
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the course is to teach undergraduates the basics and practical aspects of business models related to blockchain technology. Content: Blockchain-based business models. Decentralized platforms, markets, supply chain management, financial services and other applications. Blockchain economics. Economic aspects of the blockchain, economic incentives and rewards, voting and decision-making mechanisms based on the blockchain. Financial models and business models. Regulation and legal aspects. The use of blockchain in various industries.
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Introduction to web3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The aim of the course is to develop the ability to understand the basic principles of decentralization and apply web3 technologies to create various types of decentralized applications. The course covers the following aspects:
Web3 and decentralization. Ethereum and smart contracts. The basics of smart contracts and their role in the development of decentralized applications on Ethereum. Web3.js and other libraries. Integration with web applications. Methods for integrating decentralized functions into web applications
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Deep Learning
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to use the methods of deep neural networks for the analysis of big data.
Course content: The place of deep learning in the context of statistics and machine learning. Training and validation of deep models. Deep learning models and their applications. Architecture of deep neural networks. Setting hyperparameters. Convolutional neural networks. Recurrent neural networks. Acceleration of training of convolutional neural networks.
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Decentralized applications
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the ability to design, develop, deploy and test decentralized applications based on blockchain technology. Contents: Fundamentals of decentralization. The concept of decentralization and various approaches to its implementation in applications. Blockchain technology. Basic principles of blockchain technology and its role in decentralized applications. Ethereum, EOS, NEO decentralized application platforms and others. Development of decentralized applications. Methods and tools for developing decentralized applications.
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Cloud Computing for Data Science
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the discipline is to develop the ability to apply the MapReduce model and virtual machine computing environments for scalable data processing. Course content: Parallel programming using MapReduce. Clouds with infrastructure, platform and software as a service. Technologies and tools of virtualization. Cloud data storage. NoSQL databases and parallel query processing. Processing streaming data.
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Basics of Blockchain Engineering
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the discipline is to develop the ability to develop and deploy blockchain applications using Solidity for various blockchain platforms.
Course content: Introduction to blockchain technology. Principles of operation of distributed registries. Consensus mechanisms used in blockchain. Creation and deployment of smart contracts. The process of creating and deploying blockchain applications. Basic principles of cryptography used in blockchain.
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Data Science Applications for Industry (project)
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The aim of the discipline is to develop the ability to carry out practical data analysis projects in collaboration with industry partners. Content of discipline: Research methods. Literature analysis. Scope estimation and project planning. Collection and pre-processing of data. Exploratory data analysis. Choice, development of data analysis algorithm. Development and selection of functions, construction and evaluation of models. Interpretation and visualization of results. Presentation of the results of data analysis and preparation of a report.
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Data Collection, Visualization and Analysis
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the course is to develop the ability to apply effective data collection strategies, data visualization, analysis and interpretation techniques to obtain meaningful conclusions in data-driven decision making. Course content: Data collection methods. Principles and tools of data visualization. Exploratory data analysis. Methods of statistical analysis. Analysis and interpretation of data. Data mining. Predictive modeling and machine learning methods for data-driven decision making. ethical awareness. Data security and privacy
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Statistics for data science
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The aim of this discipline is to develop the ability to apply statistical procedures for data analysis using programming languages.
Content of the discipline: Data distributions and sampling. Confidence intervals. Hypothesis testing. Statistical experiments and significance testing. Regression and prediction. Linear regression models, analysis of variance. Classification. Statistical machine learning. Unsupervised learning.
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Big Data Management
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The aim of this discipline is to develop the ability to manage big data, design, and implement tools necessary for working with large datasets. Content of the discipline: Big data ecosystem. Big data analysis tools. Distributed database management systems. Designing big data management systems. NoSQL databases. Key-value stores. Distributed file systems. Distributed data processing. Stream management and processing.
Data for 2021-2024 years
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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.
Data for 2021-2024 years
