Infological Modelling Methods in Finance

Entry requirements: Basics of mathematics, statistic, finance, programming skills and experience working with databases

Credits: 3

Course: Elective

Language of the course: Russian

Lecturer

Oleg Metsker

Objectives

Students will learn:

– key principles and approaches to designing up-to-date databases
– principles of designing complex objects and elements of structural analysis
– application of models, methods and architecture of storing and processing Big Data
– methodologies of developing and implementing external, conceptual information, logical database model in big data

An important section of the course is learning the requirements for information security and business continuity when designing databases, as well as the requirements for providing information security of banking systems and client's personal data.

Contents

  • Stages of developing databases: Main stages of designing databases. Infological, datalogical design. Principles of designing complex objects and elements of structural analysis. Principles of multiple stages, decomposition, project stages of implementing models of storing and procesing Big Data in finance.
  • Classification and components of data models: External model of the domain. External and information (conceptual) data model. Internal level is represented by logical and physical models with which the database structure is defined. Logical data model as the access to data from the external applications.
  • Entity - Relationship model: Structuring the object system of the domain and its semantic description. Types of entities, kinds of attributes, kinds of relationships between types of entities. Data model specification.
  • Modelling and uniting local representations: Decomposition of the domain and analysis of external model. Modelling stages of local representation. Determining content, attributes, keys, relationships of the objects and their documentation.
  • Specifics of processing and storing Big Data in finance: Prospects of Big Data development in finance. Criteria of projects in Big Data. Increasing operational efficiency. Improving client service quality. Risk management and compliance. Examples of using solutions for analyzing large amounts of data in banks.
  • Database security: Requirements for information security and business continuity when designing databases. Providing information security of banking systems and client's personal data.

Format

Lectures and labs

Assessment

Attendance is mandatory.