Summary of Degree Programme
01.03.02 Applied Mathematics and Informatics
No
4 года 
Full-time, 240 credits
ENG
Instruction in English
Bachelor
No
With online tools
2026/2027 Academic year
Data Science in Business
KEO-1. Has English language proficiency at no lower than B2 level according to the Common European Framework of Reference for Languages (CEFR), and is able to study at postgraduate level or communicate professionally in English.
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-1. Demonstrates the ability to collect, process, and interpret data from contemporary scientific research in mathematics and computer science, as required to draw conclusions from relevant research.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies.
The "Data Analysis in Business" specialization is designed for students who want to learn how to work with data and use it to support and inform management decisions: optimizing business processes, evaluating investment projects, and developing and implementing business strategies. Within this specialization, students study both standard data analytics disciplines and subjects related to business management and development. This prepares them for professional careers as data analysts, managers, or entrepreneurs.
Data Science in Applied Research
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies
The "Data Analysis in Applied Research" specialization is designed for students who want to build a career in research departments of major technology and engineering companies, analytical centers, or academic laboratories. The program focuses on in-depth training in research methodology, statistical modeling, and machine learning for solving research problems and creating new knowledge and technologies. Within the specialization, students study advanced analytical methods and gain hands-on experience working on real research projects, which prepares them for professional careers as data researchers, analysts in R&D departments, or research scientists.
Year 3
· Methods of Mathematical Modeling (modules 1–2)
· Applied Statistics for Machine Learning (modules 3–4)
· Research Seminar "Data Science in Applied Research" (modules 1–4)
Year 4
· Classical and Neural Modeling (modules 1–3)
· Research Seminar "Data Science in Applied Research 2" (modules 1–3)
Data Science in Finance
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies
The "Data Analysis in Finance" specialization trains highly qualified analysts and developers capable of solving the most complex challenges of modern financial markets at the intersection of quantitative methods and advanced artificial intelligence technologies. The program provides a deep understanding of classical financial models and integrates them with modern machine learning and deep learning (ML/DL) methods. Students will master not only fundamental concepts such as derivatives pricing and risk management, but also learn to apply Reinforcement Learning (RL) for algorithmic trading, NLP for analyzing financial news and market sentiment, and Generative Adversarial Networks (GAN) for generating synthetic data and modeling market scenarios.
The specialization has a strongly pronounced practical orientation. All theoretical knowledge is reinforced through work with real financial data, writing trading robots, and implementing research projects in collaboration with industry partners.
Specialization Objective
The objective of the specialization is to equip students with a comprehensive set of competencies for building a successful career as a quantitative analyst (quant) or data scientist in investment banks, hedge funds, fintech companies, and regulatory bodies. Graduates will be able to:
· Develop, implement in Python, and critically evaluate complex quantitative models for trading, portfolio management, and risk assessment.
· Apply advanced machine learning methods (LSTM, transformers, GAN, RL) to analyze structured and unstructured financial data.
· Understand and account for the limitations of classical financial models (such as Black-Scholes) and modify them to operate under real market conditions.
Year 3
· Financial Mathematics (modules 1–2)
· Risk Management in Bank (modules 3–4)
· Research Seminar "Data Science in Financial Economics" (modules 1–4)
Year 4
· Asset Pricing and Financial Markets (modules 1–3)
· Research Seminar "Data Science in Financial Markets" (modules 1–3)
2025/2026 Academic year
Data Science in Business
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies.
Data Science in Applied Research
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies.
The "Data Analysis in Applied Research" specialization is designed for students who want to build a career in research departments of major technology and engineering companies, analytical centers, or academic laboratories. The program focuses on in-depth training in research methodology, statistical modeling, and machine learning for solving research problems and creating new knowledge and technologies. Within the specialization, students study advanced analytical methods and gain hands-on experience working on real research projects, which prepares them for professional careers as data researchers, analysts in R&D departments, or research scientists.
Year 3
· Methods of Mathematical Modeling (modules 1–2)
· Applied Statistics for Machine Learning (modules 3–4)
· Research Seminar "Data Science in Applied Research" (modules 1–4)
Year 4
· Classical and Neural Modeling (modules 1–3)
· Research Seminar "Data Science in Applied Research 2" (modules 1–3)
Data Science in Finance
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies
The "Data Analysis in Finance" specialization trains highly qualified analysts and developers capable of solving the most complex challenges of modern financial markets at the intersection of quantitative methods and advanced artificial intelligence technologies. The program provides a deep understanding of classical financial models and integrates them with modern machine learning and deep learning (ML/DL) methods. Students will master not only fundamental concepts such as derivatives pricing and risk management, but also learn to apply Reinforcement Learning (RL) for algorithmic trading, NLP for analyzing financial news and market sentiment, and Generative Adversarial Networks (GAN) for generating synthetic data and modeling market scenarios.
The specialization has a strongly pronounced practical orientation. All theoretical knowledge is reinforced through work with real financial data, writing trading robots, and implementing research projects in collaboration with industry partners.
Specialization Objective
The objective of the specialization is to equip students with a comprehensive set of competencies for building a successful career as a quantitative analyst (quant) or data scientist in investment banks, hedge funds, fintech companies, and regulatory bodies. Graduates will be able to:
· Develop, implement in Python, and critically evaluate complex quantitative models for trading, portfolio management, and risk assessment.
· Apply advanced machine learning methods (LSTM, transformers, GAN, RL) to analyze structured and unstructured financial data.
· Understand and account for the limitations of classical financial models (such as Black-Scholes) and modify them to operate under real market conditions.
Year 3
· Financial Mathematics (modules 1–2)
· Risk Management in Bank (modules 3–4)
· Research Seminar "Data Science in Financial Economics" (modules 1–4)
Year 4
· Asset Pricing and Financial Markets (modules 1–3)
· Research Seminar "Data Science in Financial Markets" (modules 1–3)
2024/2025 Academic year
Data Science in Business
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies.
Data Science in Applied Research
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies
The "Data Analysis in Applied Research" specialization is designed for students who want to build a career in research departments of major technology and engineering companies, analytical centers, or academic laboratories. The program focuses on in-depth training in research methodology, statistical modeling, and machine learning for solving research problems and creating new knowledge and technologies. Within the specialization, students study advanced analytical methods and gain hands-on experience working on real research projects, which prepares them for professional careers as data researchers, analysts in R&D departments, or research scientists.
Year 3
· Methods of Mathematical Modeling (modules 1–2)
· Applied Statistics for Machine Learning (modules 3–4)
· Research Seminar "Data Science in Applied Research" (modules 1–4)
Year 4
· Classical and Neural Modeling (modules 1–3)
· Research Seminar "Data Science in Applied Research 2" (modules 1–3)
Data Science in Finance
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies
The "Data Analysis in Finance" specialization trains highly qualified analysts and developers capable of solving the most complex challenges of modern financial markets at the intersection of quantitative methods and advanced artificial intelligence technologies. The program provides a deep understanding of classical financial models and integrates them with modern machine learning and deep learning (ML/DL) methods. Students will master not only fundamental concepts such as derivatives pricing and risk management, but also learn to apply Reinforcement Learning (RL) for algorithmic trading, NLP for analyzing financial news and market sentiment, and Generative Adversarial Networks (GAN) for generating synthetic data and modeling market scenarios.
The specialization has a strongly pronounced practical orientation. All theoretical knowledge is reinforced through work with real financial data, writing trading robots, and implementing research projects in collaboration with industry partners.
Specialization Objective
The objective of the specialization is to equip students with a comprehensive set of competencies for building a successful career as a quantitative analyst (quant) or data scientist in investment banks, hedge funds, fintech companies, and regulatory bodies. Graduates will be able to:
· Develop, implement in Python, and critically evaluate complex quantitative models for trading, portfolio management, and risk assessment.
· Apply advanced machine learning methods (LSTM, transformers, GAN, RL) to analyze structured and unstructured financial data.
· Understand and account for the limitations of classical financial models (such as Black-Scholes) and modify them to operate under real market conditions.
Year 3
· Financial Mathematics (modules 1–2)
· Risk Management in Bank (modules 3–4)
· Research Seminar "Data Science in Financial Economics" (modules 1–4)
Year 4
· Asset Pricing and Financial Markets (modules 1–3)
· Research Seminar "Data Science in Financial Markets" (modules 1–3)
2023/2024 Academic year
Data Science in Business
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies.
Data Science in Applied Research
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies.
The "Data Analysis in Applied Research" specialization is designed for students who want to build a career in research departments of major technology and engineering companies, analytical centers, or academic laboratories. The program focuses on in-depth training in research methodology, statistical modeling, and machine learning for solving research problems and creating new knowledge and technologies. Within the specialization, students study advanced analytical methods and gain hands-on experience working on real research projects, which prepares them for professional careers as data researchers, analysts in R&D departments, or research scientists.
Year 3
· Methods of Mathematical Modeling (modules 1–2)
· Applied Statistics for Machine Learning (modules 3–4)
· Research Seminar "Data Science in Applied Research" (modules 1–4)
Year 4
· Classical and Neural Modeling (modules 1–3)
· Research Seminar "Data Science in Applied Research 2" (modules 1–3)
Data Science in Finance
KEO-2. Understands the fundamental principles of working with data and has advanced proficiency in modern data analysis tools, including programming and algorithmic skills, as well as mathematical methods for solving data analysis problems.
KEO-3. Has a sound knowledge of the fundamentals of mathematical analysis, linear algebra, discrete mathematics, probability theory, and mathematical statistics. Is proficient in the principal methods of optimisation.
KEO-4. Understands the fundamental principles underlying the design and operation of modern computers and operating systems.
KEO-5. Has knowledge of at least two programming languages.
KEO-6. Understands fundamental algorithms and data structures and is able to design efficient algorithms.
KEO-7. Has a sound understanding of the fundamentals of machine learning and mathematical modelling.
PC-2. Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model.
PC-3. Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases.
PC-4. Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science.
PC-5. Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies.
The "Data Analysis in Finance" specialization trains highly qualified analysts and developers capable of solving the most complex challenges of modern financial markets at the intersection of quantitative methods and advanced artificial intelligence technologies. The program provides a deep understanding of classical financial models and integrates them with modern machine learning and deep learning (ML/DL) methods. Students will master not only fundamental concepts such as derivatives pricing and risk management, but also learn to apply Reinforcement Learning (RL) for algorithmic trading, NLP for analyzing financial news and market sentiment, and Generative Adversarial Networks (GAN) for generating synthetic data and modeling market scenarios.
The specialization has a strongly pronounced practical orientation. All theoretical knowledge is reinforced through work with real financial data, writing trading robots, and implementing research projects in collaboration with industry partners.
Specialization Objective
The objective of the specialization is to equip students with a comprehensive set of competencies for building a successful career as a quantitative analyst (quant) or data scientist in investment banks, hedge funds, fintech companies, and regulatory bodies. Graduates will be able to:
· Develop, implement in Python, and critically evaluate complex quantitative models for trading, portfolio management, and risk assessment.
· Apply advanced machine learning methods (LSTM, transformers, GAN, RL) to analyze structured and unstructured financial data.
· Understand and account for the limitations of classical financial models (such as Black-Scholes) and modify them to operate under real market conditions.
Year 3
· Financial Mathematics (modules 1–2)
· Risk Management in Bank (modules 3–4)
· Research Seminar "Data Science in Financial Economics" (modules 1–4)
Year 4
· Asset Pricing and Financial Markets (modules 1–3)
· Research Seminar "Data Science in Financial Markets" (modules 1–3)
1. Studying at the faculty established by HSE and leading Russian IT company Yandex.
2. High-quality teaching.
3. Obtaining professional competences for a specific industry.
4. Rigorous project activities and research.
5. Wide network of partners.
The programme allows graduates to quickly immerse themselves in a career anywhere in a global world. Also, they are capable of developing effectively over the course of their entire lives and are prepared for a wide range of professional trajectories, including data analysis, business analysis, financial analysis, systems analysis, developer work, IT consulting, and various IT specializations.
The programme’s graduates can find work as top specialists and experts at data-driven financial firms and software development companies. They can also engage in the development and set-up of information systems at major Russian and international companies thanks to their understanding of system architecture and ability to work with Big Data, as well as their skills in uncovering hidden dependency issues and specific information in a given field.
The programme consists of several key components: a professional cycle, project and research work, and additional economic field, humanities subjects, English language instruction, and physical training.
During their first two years of study, students learn the fundamentals of mathematics and programming. The list of mathematical courses includes all key areas for computer specialists such as discrete mathematics, mathematic analysis, linear algebra and geometry, probability theory and statistics, as well as differential equations.
The programme’s mandatory courses have been developed in such a way, so that, by the end of their second year, students will be able to independently carry out a substantive programming project. As part of three mandatory courses, programming is presented from several perspectives. Initially, students learn about the fundamentals by taking the course “Basics and Methodologies of Programming” and learn Python and C++ languages. Then, they move on to the course “Algorithms and Data Structures”, which considers programming from a more theoretical point of view (e.g., how to approach problems, design and assess algorithms, identify key algorithms, etc.). Further on, students shall progress to the engineering side of this subject by taking the “Computer and Operational Systems Architecture” course, where they learn about how programs interface with computer systems, as well as about the operations of compilers and translators and the various components of operating systems. During their second year, students will have a chance to carry out an individual programming project, whereby, under the tutelage of a mentor (from the university or a partner IT firm in the finance sector), they will enhance their knowledge in programming, as well as gain valuable experience in developing software products.
During the third and fourth years of study, special attention is focused on applied analysis of data and machine learning in the financial sector. For instance, while studying the Business Analysis and Machine Learning courses, students will get to learn about methods and algorithms, relying on real-life examples taken from relevant sectors (e.g., financial risks, client predictors, scoring models, recommender systems, insurance processes, etc.).
Project work and research activities take up a fifth of the entire programme. This work includes a programming project, term paper, thesis, research seminars, as well as academic internships, industrial placement and pre-graduation internships.
The programme is designed to train researchers and analysts in computer science and artificial intelligence, as well as software engineers specializing in data processing and analysis and the development of fault-tolerant systems.
Students study a wide range of subjects in fundamental mathematics, programming, and algorithms. In the later years of the program, students are offered an exceptionally broad choice of courses and specializations, allowing them to explore cutting-edge areas of computer science—from artificial intelligence and engineering to theoretical computer science.
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PC-1 |
Demonstrates the ability to collect, process, and interpret data from contemporary scientific research in mathematics and computer science, as required to draw conclusions from relevant research. |
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PC-2 |
Demonstrates the ability to develop and implement, in the form of a software module, an algorithm for solving a given theoretical or applied problem based on a mathematical model. |
|
PC-3 |
Demonstrates the ability to develop software and information systems for computer systems, services, computing facilities, and databases. |
|
PC-4 |
Demonstrates the ability to analyse, write, and edit academic and technical texts in Russian and a foreign language for professional and research purposes in the fields of mathematics and computer science. |
|
PC-5 |
Demonstrates the ability to present the results of their academic and professional activities clearly, effectively, and convincingly in public, using appropriate arguments and modern information and communication technologies. |
Admission to the specialisations is based on a competitive selection process according to the following criteria:
* Cumulative academic ranking — the primary criterion. Minors and humanities courses are not taken into account.
* Additional factors — considered when the cumulative ranking is not sufficiently high:
* positive progress in academic performance;
* recommendations from teaching staff;
* results of relevant coursework and project assignments.
This degree programme of HSE University is adapted for students with special educational needs (SEN) and disabilities. There is a specially designed Physical Education course available for such students (Syllabus of the adapted Physical Education course). Special assistive technology and teaching aids are used for collective and individual learning of students with SEN and disabilities. The specific adaptive features of the programme are listed in each subject's full syllabus and are available to students through the online Learning Management System.
All documents of the degree programme are stored electronically on this website. Curricula, calendar plans, and syllabi are developed and approved electronically in corporate information systems. Their current versions are automatically published on the website of the degree programme. Up-to-date teaching and learning guides, assessment tools, and other relevant documents are stored on the website of the degree programme in accordance with the local regulatory acts of HSE University.
I hereby confirm that the degree programme documents posted on this website are fully up-to-date.
Vice Rector Sergey Yu. Roshchin
Summary of Degree Programme 'Data Science and Business Analytics'
