Course Details
The MSc in Business Intelligence and Analytics is validated by the University of Westminster (UK) in November 2019. This course is one of most popular postgraduate courses in contemporary international higher education market and takes you to the world of modern data science that addresses the business need to advance data organisation and information gathering, go insight into information and exploit knowledge hidden in routinely collected data in order to improve decision-making. The course is more technology focused, and stretches the data-mining and decision-sciences theme to the broader agenda of business intelligence.
Throughout your learning within this course, you will be working with the team of dedicated, dynamic and research active academic staff who are committed to providing high quality research-led teaching with practical classes. You will benefit from open talks and presentations of visiting lecturers - a range of e-business and IT industry specialists, and various datathon forums where you may present your research findings to leading business people and professionals.
Typically graduates of the course will be employed as consultants, decision modelling or advanced data analysts, members of technical/analytics teams supporting the decision making of middle and top management in different sizes of organisation operating in diverse sectors. Graduates will be expected to work in e-business, retail head offices, public sector organisations such as public administration bodies and health service management, hosts of financial and banking institutions, brokers and regulators, along with all the specialist support consultancies in IT and market research and forecasting, all of whom use data for the full range of decision making.


The graduates may also pursue further research career exploiting doctorate study opportunities provided by WIUT, the University of Westminster or at other higher education institutions in Uzbekistan or internationally. 

The course provides a balanced study, which aims at producing graduates that are capable of:
  • Thinking in a systematic and methodological way about Business Intelligence & Analytics issues;
  • Utilising their problem-solving skills and their knowledge of various techniques, tools and methods, to deliver Business Intelligence & Analytics solutions to a wide range of problems;
  • Creating models and deploying appropriate software tools that satisfy specified requirements, and testing their use in a target domain;
  • studying the context within which the design of Business Intelligence & Analytics takes place, i.e. as part of the range of strategic, managerial and operational activities involved in the gathering, processing, storage and distribution of information;
  • Identifying the security and legal implications of Business Intelligence & Analytics applications, e.g. Customer Relations Management (CRM);
  • Independent in-depth analysis of a chosen topic making use of information resources outside a teaching environment.

Entry requirements

Formal Higher Education

Applicants should normally hold an Undergraduate degree (or equivalent) from a recognised higher education institution with a minimum of a second lower class honours (2:2 or equivalent). Applicants without a formal HE qualification or the formal qualification is not at the equivalent academic standard, may be considered if the following conditions apply.

  • They are or have been in employment where their employed role is in the area of the course and involves a high level of analysis and critical thinking. If so such candidates will be required to provide evidence of such employment, its nature and level. This evidence will be considered at Interview and the decision of the panel (see below) will be final.

Core Modules

Big Data Theory and Practice

The module discusses how to manage the volume, velocity and variety of Big Data, SQL and non-SQL databases, and it touches on issues related to data governance and data quality. The module will also introduce students to data science tools and technologies such as Python, Matlab, Apache Spark, JavaScript Amazon AWS Machine Learning and MS Azure Machine Learning as well as to private and sensitive data management approaches and open data framework.

Optional Modules

Business Optimisation
The module provides an in-depth analysis of advanced topics in operational research (OR) such as discrete optimisation, multiple criteria optimisation and modern heuristic approaches. Taking this module, students will deal with real-world optimisation problems in a business context implementing computer-based models, and evaluate business model solutions, their usefulness for decision-making and make sound recommendations.