Educational program

More details
Code – Speciality

7M06106 - Big data
The aim of the educational program

Training specialists who are able to apply the systemic principles of designing, developing and supporting complex information systems based on methodological and technological solutions, conduct innovative scientific research to detect knowledge in systems with big data.
Graduate model
Graduate qualification Professional sphere:

- conducting research in the field of big data analytics, stochastic optimization, predictive modeling, forecasting, enterprise data management, business analysis, using mathematical and technological knowledge and skills to select, evaluate, analyze and use tools and technology of big data;
- planning work on system analysis and monitoring their implementation;
- management activities in the implementation of big data systems and services based on big data technology
The object of professional occupation:

- organizations of various forms of ownership, industry and business that are developing and - use of information systems, intelligent products and services based on artificial intelligence technologies and scientific achievements in the field of
intelligent methods of big data analysis and machine learning;
- higher education organizations, professional educational organizations;
- scientific, research, scientific and industrial organizations related to solving problems of data mining;
- government departments
Types of professional occupation:

- research;
- organizational and management;
- design and technological;
- design and engineering;
- production and technological;
- experimental research;
- service-operational, etc.
Functions of professional occupation:

- Development of new models of enterprise information infrastructure, taking into account the capabilities of big data technologies;
- Introduction and evaluation of the effectiveness of technologies and tools of big data at the enterprise;
- implementation and application of analytics and decision support tools based on big data technologies, implementation of decision management
Graduate competences map
Formed core competencies Program outcome
1. Ability to apply distributed data processing and storage technologies. Knowledge of the basic principles of Kubernetes. Ability to deploy Kubernetes applications using the CLI. Apply distributed data processing and storage technologies. Know the basic principles of Kubernetes. Deploy Kubernetes applications using the CLI.
2. The ability to identify the main problems in the field of philosophy and methodology of science; describe current relevant methodological, methodological and philosophical problems in the field of IT Identify the main problems in the field of philosophy and methodology of science; describe current relevant methodological, methodological and philosophical problems in the field of IT
3. Knowledge of cloud computing technology: software architectures, virtualization and container. Ability to configure VPS and VPN server, run applications based on Docker Know cloud computing technologies: software architectures, virtualization, and container. Configure VPS and VPN server, run Docker-based applications
4. Ability to use standard technologies and data analytics tools, such as MapReduce, Hadoop, NoSQL, R languages, Python Use typical technologies and data analytics tools such as MapReduce, Hadoop, NoSQL, R, Python
5. The ability to analyze existing methods of data visualization, create a data visualization application using existing technologies and tools Analyze existing data visualization methods, create a data visualization application using existing technologies and tools
6. The ability to formally represent the semantic content of sentences and discourses in NL Formally represent the semantic content of sentences and discourses in NL, describe various classes of objects using the project languages Semantic Web RDF and RDFS
7. Ability to develop big data system architecture and hardware and software infrastructure Develop big data system architecture and hardware and software infrastructure, analyze large data sets using Scala and Spark
8. Ability to solve psychological problems in the process of managerial activity, make managerial decisions, evaluate their possible consequences Solve psychological problems in the process of managerial activity, make managerial decisions, evaluate their possible consequences
9. The ability to organize the collection, analysis and systematization of scientific and technical information on the topic of research, to apply scientific methods in research Organize the collection, analysis and systematization of scientific and technical information on the topic of research, apply scientific methods in research
10. Proficiency in a foreign language at the level of international standards C1-C2 and the grammatical characteristics of the scientific style; ability to work in an international environment Proficient in a foreign language at the level of international standards C1-C2 and the grammatical characteristics of the scientific style; be able to work in an international environment
11. Possession of basic knowledge in pedagogy and psychology in higher education, knowledge of the modern paradigm of higher education Possess basic knowledge of pedagogy and psychology in higher education, know the modern paradigm of higher education
12. Knowledge of the basic methods and technologies of data mining. The ability to apply approaches and algorithms for solving data analysis problems to solving real problems. Know the basic methods and technologies of data mining. Apply approaches and algorithms for solving data analysis problems to solving real problems.
13. The ability to develop and use software solutions using modern environments and programming languages (R, Python) to perform the analysis of large data arrays Develop and use software solutions using modern environments and programming languages (R, Python) to perform analysis of large data arrays
14. Knowledge of the main elements of the big data analysis process, the ability to integrate data from different sources, interpret in the context of the task, analyze the results Know the main elements of the big data analysis process, integrate data from different sources, interpret in the context of the task, analyze the results
15. The ability to apply supervised learning algorithms to predict and evaluate results; apply unsupervised learning algorithms to data analysis tasks and evaluate results Apply supervised learning algorithms to predict and evaluate results; apply unsupervised learning algorithms to data analysis tasks and evaluate results
Modular Curriculum
Base disciplines Profiling disciplines Additional types of study