System Analysis and Data Science is a fundamental and high-tech interdisciplinary field at the intersection of applied mathematics, computer science, artificial intelligence (Data Science / AI), and control theory. It studies the principles of investigating complex objects and processes as unified systems in the interconnection of their elements, internal, and external factors.

In an era of exponentially growing data volumes (Big Data), global market instability, and management digitalization, a specialist in this major is a systems engineer, Data Scientist, complex systems analyst, and developer of Decision Support Systems (DSS), capable of transforming raw data into strategic decisions and optimizing operations for organizations of any scale.

The training of specialists in System Analysis and Data Science is based on studying the following objects:

- complex systems of various natures: information, economic, financial, socio-political, technical, organizational, ecological, logistical, and security systems;

- Data Science methods and tools: machine learning and deep learning algorithms (Machine Learning / Deep Learning), data mining (Data Mining), methods for collection, cleaning, visualization, and processing of large data arrays (Big Data);

- modeling and forecasting: mathematical, stochastic, simulation, system-dynamic, and network modeling of complex systems and processes under risk and uncertainty;

- decision-making and optimization: decision theory, multi-criteria optimization, game theory, operations research, analytical and innovative management methods (Operations Research);

- complex information systems: design, architecture, implementation, and maintenance of analytical and management information systems (BI, ERP, CRM, DSS).

Learning goals by higher education levels:

First (Bachelor's) level — training specialists capable of developing and applying system analysis methods and tools to solve complex problems in various fields of activity.

Main focuses of Bachelor's degree training:

- mastering fundamental mathematical training (higher mathematics, differential equations, probability theory, mathematical statistics, higher algebra, discrete mathematics);

- mastering basic concepts of system analysis, systems theory, mathematical modeling, and optimization methods;

- practical acquisition of skills in modern programming languages and data analysis tools (Python, R, SQL, C++), working with databases (SQL/NoSQL), and GIS;

- applying Data Science methods for exploratory data analysis, data visualization, time series forecasting, and business process automation.

Second (Master's) level — training professionals capable of designing complex information systems, developing new methods, and applying existing system analysis methods to solve complex problems in various fields under conditions of high uncertainty and risk.

Main focuses of Master's degree training:

- designing the architecture of complex distributed analytical and information retrieval systems (Business Intelligence, Big Data Platforms);

- developing custom machine learning models, artificial intelligence systems, using deep neural networks to forecast complex crisis phenomena;

- strategic system analysis, risk management, system dynamics modeling (System Dynamics), and evaluating system resilience;

- managing data analysis and systems development projects, conducting scientific and applied research, consulting top management.

Key competencies of a graduate:

System modeling — building analytical, simulation, and conceptual models of complex systems (using UML, BPMN, System Dynamics);

Data Science & ML Engineering — developing, training, and deploying machine learning models, creating predictive-analytic systems (Python, TensorFlow, PyTorch);

Analytics and decision-making — using multi-criteria choice methods, game theory, analytic network and hierarchy processes to make optimal decisions;

Business Intelligence (BI) & Big Data — processing ultra-large datasets (Spark, Hadoop), building interactive dashboards (Power BI, Tableau), designing Data Warehouses (DWH);

Information systems design — formulating system requirements (Software Requirements Specification), designing the architecture of complex IT systems and services.

Graduates of major F4 System Analysis and Data Science are versatile and highly compensated specialists capable of working in virtually any industry involving complex systems and large volumes of data:

- data analytics and artificial intelligence: Data Scientist, Machine Learning Engineer, Data Analyst, Big Data Engineer, Product Analyst;

- business analytics and consulting: Systems Analyst, Business Analyst, BI Architect / BI Developer, IT Consultant;

- financial-investment and banking sector: Quantitative Analyst (quant), Risk Manager, Financial Market Analyst, Developer of scoring models in banks and FinTech companies;

- project and product management: IT Project Manager, Product Owner / Manager, Lead Systems Engineer;

- state and corporate governance: situation center analyst, strategic planning specialist in government bodies, think tanks, security, and defense structures;

- logistics and E-commerce: supply chain analyst, route optimization specialist, marketing analyst.

Training is provided by the Department of Higher Mathematics and System Analysis (Department 405)

Education is provided under the following educational programs:

Educational Program	Degree	Mode of Study	Duration of Study
System Analysis and Management	Bachelor based on complete general secondary education	Full-time	3 years and 10 months
System Analysis and Management	Master, professional educational program	Full-time	1 year and 4 months