Business Intelligence


Amount of credits – 5.

Forms of the educational process – lectures, laboratory classes.

Form of final control – credit.


The Business Intelligence (BI) course is topical in MSc Program because it considers solutions that allow to present, consolidate and visualize data, turning information into an effective means of doing business. The course is based on knowledge of Big Data and Data Warehousing technologies, creation of algorithms of representative samples, visualization of graphical data and presentation of tables with structured data. Together with this the Internet of Things (IoT) technologies, statistics and Web-based design with Cloud Computing orientation is also considered. The course provides knowledge in solving tasks of designing the construction of a Dashboard with visualization of heterogeneous data and presentation of tables and statistics information.

Subject learning objectives

Upon successful completion of this subject, students should be able to:

  • Apply Information Systems and Technologies for solving the problems of presenting business data on optimization tasks in management, production and commercial activity. Work with world-class software packages in the field of BI.
Course intended learning outcomes

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes – competencies:

  • Know the principles and methods of implementation of Decision Support Systems for development and innovation, including IT-innovation.
  • Make use of the special tools for data visualization to optimize business processes.
  • Know the key issues of using and design of integrated and corporate Information Systems that are based on BI in innovation activities.

Topic 1. Features of technologies of Data Mining, Data Warehousing, Business Analytics and the role and place of Business Intelligence in today's business processes and organizational structures.
Topic 2. Identifying of sources and means of obtaining primary data. Fundamentals of technologies for Monitoring, Data Warehousing and Processing of Big Data. Features of Internet-based technologies, Cloud Computing, IoT and Machine Learning (ML).
Topic 3. A practical example of using the BI Toolkit. Microsoft Power BI for visualizing business data and creating reports. Features of Cloud Computing technologies in solving BI tasks.
Topic 4. Prospects for applying multidimensional OLAP analytical queries. Application of MapReduce algorithms and technologies of Distributed Data Storages. Cognitive technologies and definitions of BI design concept.
Topic 5. Architecture of building BI-solutions. Applying JavaScript, HTML5 and CSS3 for Dashboard development and business data visualization tools. Features of integration with Cloud Services APIs (Application program interfaces).

  1. A Guide to the Business Analysis Body of Knowledge (BABOK Guide). – 3rd Edition. – IIBA. – 2015. – 502 p.
  2. Ferrari A. Introducing Microsoft Power BI / Alberto Ferrari and Marco Russo // Microsoft Press, 2016. – 189 p.
  3. Collier Michael S. Microsoft Azure Essentials: Fundamentals of Azure, Second Edition / Michael S. Collier and Robin E. Shahan // Microsoft Press, 2016. – 246 p.
  4. Barnes J. Microsoft Azure Essentials: Azure Machine Learning / Jeff Barnes
    // Microsoft Press, 2015. – 237 p.
  5. Sarkar A. Learning AWS / Aurobindo Sarkar, Amit Shah // Packt Publishing, 2015. – 237 p.
  6. Browne D. IBM Cognos Business Intelligence V10.1 Handbook / Dean Browne, Brecht Desmeijter, Rodrigo Frealdo Dumont, Armin Kamal and others // An IBM Redbooks publication, 2010. – 572 p.
  7. Ingebrigtsen M. Using Kibana for Business Intelligence [Electronic resourse]
    / Morten Ingebrigtsen. Elasticsearch Blog, 2014. – Mode of access:
  8. Sinha S. Making Big Data Work for Your Business / Sudhi Sinha // Impackt Publishing, 2014. – 170 p.
  9. Squire M. Clean Data / Megan Squire // Packt Publishing, 2015. – 272 p.
  10. Richardson L. RESTful Web APIs, [Electronic resourse] / Leonard Richardson, Sam Ruby. O'Reilly's Open Book Project, 2007. – 448 p. – Mode of access:
  11. Cuesta H. Practical Data Analysis / Hector Cuesta // Packt Publishing,
    2013. – 360 p.
  12. Stefanov S. Object-Oriented JavaScript. Second Edition/ Stoyan Stefanov, Kumar Chetan Sharma // Packt Publishing, 2013. – 382 p.
  13. Heydt M. D3.js By Example. Second Edition/ Michael Heydt // Packt Publishing, 2015. – 304 p.
  14. Vaish G. Getting Started with NoSQL / Gaurav Vaish // Packt Publishing,
    2013. – 142 p.
  15. Shaikh Mubin M. Create First OLAP Cube in SQL Server Analysis Services [Electronic resourse] / Mubin M. Shaikh. CodeProject, 2014. – Mode of access:
  16. Briggs B. Enterprise Cloud Strategy / Barry Briggs and Eduardo Kassner
    // Microsoft Press, 2016. – 109 p.