The purpose of the credit module is to form students' abilities to develop and apply models of financial, geophysical and socio-economic processes and fields using the theoretical and methodological foundations of financial and system mathematics, and students must master the following competencies: General: GC1 Ability to abstract thinking, analysis and synthesis. GC3 Ability to search, process and analyze information from various sources. Professional: FC4 Ability to assess risks, develop risk management algorithms in complex systems of various nature. FC5 Ability to model, predict and design complex systems and processes based on methods and tools of system analysis. FC6 Ability to apply Data Science theory and methods to perform data mining in order to identify new properties and generate new knowledge about complex systems. Program Learning Outcomes: PRN 2 Build and investigate models of complex systems and processes using the following methods: system analysis, mathematical, computer and information modeling. PRN 3 Apply methods of disclosure of uncertainties in the tasks of system analysis, to reveal situational uncertainties and uncertainties in the tasks of interaction, counteraction and conflict of strategies, to find a compromise in the disclosure of conceptual Uncertainty. PRN 5 Use risk assessment measures and apply them in the analysis of multifactorial risks in complex systems. PRN 6 Apply methods of machine learning and data mining, mathematical apparatus of fuzzy logic, game theory and distributed artificial intelligence to solve complex problems of system analysis. PRN 8 Identify and evaluate the parameters of mathematical models Management Objects PRN 12 Know the legislative acts to ensure the protection of intellectual property, the requirements for compliance with the established requirements when filing applications for patents for inventions; adhere to academic integrity 1.1. In particular, to acquire the following practical skills: • how to code in Python and R; • work with scientific packages such as NumPy, Scirit-Learn, Keras, etc.; • understanding of how to use the Pandas data analysis toolkit; • how to use Python and R to solve real-world problems; • how to get a job as a data scientist; • how to conduct an in-depth analysis of investments; • how to build investment portfolios; • how to calculate the risks and returns of individual securities; • application of best practices for working with financial data; • use of regression analysis; • understanding of the capital pricing model; • comparison of securities by their Sharpe ratio; • simulation using the Monte Carlo method; • ability to evaluate options using the Black-Scholes formula; • how to easily get a job as a developer in a financial institution.