The goal of the educational program is to develop highly qualified specialists who can apply analytical methods and tools to extract valuable information from data and make data-driven decisions in various interdisciplinary areas, including business, science, public and government sectors.
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.
