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Business Big Data Analytics
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Summary of the Profile
General Description:
Objective(s) of a study programme:
To provide comprehensive knowledge of mathematics, informatics and economics, to develop the ability to analyse big business datasets, identify business problems, apply the acquired knowledge and develop mathematical models and algorithms for business decision making and business insight.
Learning outcomes:
Specific Skills:
A1 Have ability to think logically and analytically.
A2 Have ability at conceptual level to identify business issues, to analyse, to plan and to forecast business actions in areas that have big data and the need to analyse it.
A3 Have ability to abstract business information, to describe processes of business systems using mathematical relationships.
A4 Have ability to deal with non-standard complex business tasks in new and unfamiliar environments combining mathematics, computer science and business knowledge.
A5 Have ability to compare several solutions for the same issue and to find the optimum way according to selected criteria.
A6 Have ability to develop algorithms and computer programs for created models realization, to work with big data.
A7 Have ability to transform heuristic arguments in mathematical proofs; prove the assertions, similar to the known.
A8 Have ability to understand mathematical statements, their proofs and interrelationships and apply them in a variety of contexts.
Knowledge and its Application:
B1 Have ability to apply mathematical knowledge related to process and event analysis, prognosis, optimization, risk assessment and big data.
B2 Have knowledge of business organizations’ operational processes, efficiency indicators, modelling principles, factors influencing decision-making, and able to use them for development of mathematical models.
B3 Can explain the importance of big data management in the organization, and able to use databases, to create and use metadata, to specify user needs and limitations of the information system.
B4 Have ability to choose the appropriate mathematical methods for the development of business big data analysis models, understand the analysis phases and their application stages, and apply it in the interdisciplinary area.
B5 Have ability to describe of the modern business analytics systems’ architecture, programming languages and their application possibilities for business big data analysis models realization given the limited computational technological resources.
B6 Have ability to integrate and apply the acquired knowledge of mathematics, computer science and business, to identify the new trends and mathematical methods and have the skills to use them developing the business big data analysis models.
Research Skills:
C1 Have ability to collect, summarize, systematize, analyze, evaluate any information regardless of its form (audio, video, text, tactile, etc.).
C2 Have ability to find, to collect and to perceive a scientific literature in mathematics and to use their knowledge of research in big data analysis tasks.
C3 Have ability methodically to justify, to plan, to organize and to perform analysis of business big data.
C4 Have ability to integrate knowledge of business, informatics and various mathematical modelling methods.
C5 Have ability to create mathematical models for analysis of business big data, to select parameters, to test model relevance for available data, to compare few models with each other.
C6 Have ability to initiate, to organize, to execute and to communicate projects, to interpret the results, and to formulate relevant conclusions and prognosis for investigated business systems.
C7 Have ability to prepare reports and analytical insights, to communicate knowledge and understanding for managers, who make business decisions.
Developed social and personal skills:
D1 Have ability to critically evaluate their own and others performance and professional experience.
D2 Have ability to make independently decisions, to considering their consequences and their complexity.
D3 Have ability to work independently and in the interdisciplinary team, to generate new ideas; to integrate knowledge and skills.
D4 Have ability to take responsibility for the results and quality.
D5 Have ability clearly and reasonably to present information for colleagues and specialists of other disciplines, have good communication skills.
D6 Have ability to choose the direction of enhancement and to develop acquired skills as needed.
D7 Have ability to recognize, uphold and nurture the most important values of the academic and professional field: justice, honesty, respect for human beings, tolerance, professional, scientific and civic responsibility.
Activities of teaching and learning:
Lectures;
Laboratory Works;
Seminar (Work in little group);
Practical Exercises (tasks);
Individual tutorial;
Practices;
Individual project and (or) team project;
Interactive learning methods;
Case Analysis (Case Study);
Coursework;
Library / Information Retrieval Tasks;
Oral Presentation and Reports.
Methods of assessment of learning achievements:
To achieve the study results should be evaluated by a ten-point criteria-referenced assessment system. It can be applied to a variety of student achievements assessment methods:
Written Examination;
Written and Oral Examination;
Test;
Laboratory Notes and Report and Laboratory Examination;
Modeling works;
Assignments;
Individual or group Project Report;
Oral and Poster Session;
Work Placement Report and examination;
Colloquium;
Control Work;
Essay, Literature Analysis;
Final paper, Examination.
Framework:
Study subjects (modules), practical training:
Core and Compulsory Subjects (60 ECTS):
Matrix Analysis, Big Data Research Project, Big Data Mining Methods, Project of Business External Data Analytics, Multivariate Statistical Analysis Models, Optimization and Decision Making, Business Risk and Uncertainty Analytics, Project of Business Internal Data Analytics, Information Systems Requirements Analysis and Specification.
Research Project and Optional Subjects (30 ECTS):
Strategic Business Analysis, Electives (Analytics of Finance and Accounting Data, Marketing Decisions Modeling, Time Series Analysis Financial Markets Models, Big Data Analytic Tools, Financial Management Decisions, Consumer Analytics, Business Information Technology.
Final Degree Project (30 ECTS).
Specialisations:
-
Optional courses:
Distinctive features of a study programme:
A graduate has comprehensive and relevant interdisciplinary knowledge in mathematics, computer science, business and is able to apply it for the analysis of big data in business. The graduate is able to integrate and apply the acquired skills for business insights by creating and developing business strategies and models aimed to predict the demands and behaviour of competitors, customers and suppliers, optimize sales and channels of distribution, as well as consumption of resources, analyse, measure and predict financial results and risk.
Access to professional activity or further study:
Access to professional activity:
The graduate can work as an analyst in big data and business systems, consultant for solutions, risk assessment and management positions in various business organisations (e-commerce, transportation, logistics, pharmaceutics, health care, finances, insurance, telecommunications) and governmental institutions.
Access to further study:
The graduate has access to the third cycle studies.
