Кто читает:: Кафедра бизнес-аналитики

Статус:: Курс по выбору

Когда читается:: 1-й курс, 2, 3 модуль

Полная версия программы учебной дисциплины

Аннотация

The objective of the discipline mastering is developing the students' knowledge of the complex of theoretical and methodological foundations in the field of business intelligence, as well as the practical skills needed for the implementation and practical use of such systems. As a result of the discipline mastering the student must: know the characteristics of the market of business intelligence systems and prospects of development of information systems for management, basic classes and principles of information systems development, which are in use for practical purposes; be able to apply business intelligence information systems for solving problems of management accounting, management reporting, financial and economic analysis; have the skills (gain experience) in analysis of trends in global and Russian market of business intelligence information systems and best practices of their application in different economic sectors.

Цель освоения дисциплины

The objective of the discipline mastering is developing the students' knowledge of the complex of theoretical and methodological foundations in the field of business intelligence, as well as the practical skills needed for the implementation and practical use of such systems.

Планируемые результаты обучения

Know the characteristics of the market of business intelligence systems and prospects of development of information systems for management, basic classes and principles of information systems development, which are in use for practical purposes
Be able to apply business intelligence information systems for solving problems of management accounting, management reporting, financial and economic analysis
Have the skills (gain experience) in analysis of trends in global and Russian market of business intelligence information systems and best practices of their application in different economic sectors

Содержание учебной дисциплины

Data warehouse for business structures
The purpose of the appearance of data warehouses on the market of software. Repository as a response to the need for business structures in the reporting. Storage as a response to the need of business structures in operational ad-hoc analysis. Data stores as a tool monetization of historical data about business development. The reasons for the jump of the needs of the data center in the XXI century. Hardware and software are main prerequisites for the appearance of data stores. The accumulation of a critical amount of data is historical background of the appearance of data stores. Widespread problem situation in companies is «zoo systems». The term «data warehouse»: the evolution of the understanding. The required elements of data stores are the current representation. The old view data warehouse and data marts. Normalization and de-normalization is the key concepts for data warehousing. Risks store update. Staging is as a solution Big industrial data warehouse: the important elements. «Data warehouse»: the key requirements for data storage. The physical model of the data warehouse: the basic techniques of designing. Third normal form is the balance between normalization and de-normalization. SCD is slowly changing dimension. Data Vault is a new ideology warehousing. Market data storages of the Russian Federation: the market capacity, leaders and participants. ETL – tools formation of storage. MS SSIS. ODI.
OLAP-systems and other BI-solutions
Types of applications available in Oracle Hyperion Essbase. Block Storage and Aggregate Storage. MDX. Build cubes Oracle Hyperion Essbase from a relational star schema with Oracle Hyperion Analytic Integration Services. Data analysis in Excel, using the add-Essbase Spreadsheet Add-In for Excel. Use Oracle Hyperion Web-Analysis for data analysis. Building reports in Oracle Hyperion Financial Reporting. Use Oracle Hyperion Interactive Reporting for complex data analysis.
Data Mining Systems
Data Mining – demand for business cases solutions, prerequisites. Business cases, which were successful solves by data mining, classification of tasks: Directed Data Mining, supervised learning: classifications, predictions, estimation. Undirected Data Mining, learning without a teacher: description and visualization, clustering, association rules search. Methodology of Data Mining projects: CRISP-DM, description of the phases, the justification for the phases. Practical examples of Data Mining projects: timing, cost, method of estimating the success. Types of Data Mining algorithms: algorithms for classes separation, the problem of conversion training, forecasting of trends, approaches to clustering, search of association rules, correlation analysis, the case of symptoms selection. Data Mining tools: market of Data Mining tools, recommendations for Data Mining projects’ infrastructure and tools selection. Data Mining algorithms, plugged-in SQL Server 2008 R2, advantages, disadvantages, typical examples of usage. MS Excel 2010 as effective DataMining tools for solving real business cases. Professional Data Mining tools: Matlab, Veca, SPSS, etc.

Элементы контроля

Controlwork
Homework
Classroom
Exam

Промежуточная аттестация

Промежуточная аттестация (3 модуль)
0.15 * Classroom + 0.15 * Controlwork + 0.5 * Exam + 0.2 * Homework

Магистерская программа «Бизнес-информатика: цифровое предприятие и управление информационными системами»

Контакты

Аккредитовано АПКИТ

Системы бизнес-интеллекта

Программа дисциплины

Аннотация

Цель освоения дисциплины

Планируемые результаты обучения

Содержание учебной дисциплины

Элементы контроля

Промежуточная аттестация

Список литературы

Рекомендуемая основная литература

Рекомендуемая дополнительная литература