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Learning Opportunity: Business Big Data Analytics

Business Big Data Analytics

Course Information

Objective(s) of a study programme:
To prepare qualified specialists: with deepen knowledge in mathematics, computer science and business, enabling possibility to analyse business big data; able to identify at conceptual level the business issues in traditional and global organizations based on networking, to creatively apply the acquired knowledge in new situations, to develop mathematical models for relevant business decision-making, to critically evaluate data and results, as well as able to communicate information for audience of specialists, to work in a team and to continually develop their professionalism.
Learning outcomes:
Knowledge and its application
A1. Demonstrate deepened and expanded math knowledge related to process and event analysis, prognosis, optimization, risk assessment and big data.
A2. 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.
A3. Have knowledge of specification of enterprise informational needs, purpose of metadata, 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.
A4. Have knowledge of mathematical methods applied to the development of analysis models for business big data, understand the analysis phases and their performance method, and able to apply it in the interdisciplinary area.
A5. Have knowledge of the modern systems’ architecture for business analytics, of programming languages and their application possibilities for business big data analysis models realization, and able to apply them to real tasks.
A6. Have ability to integrate and apply the acquired knowledge of mathematics, computer science and business and the skills in developing business big data analysis models.
A7. Have knowledge of new trends in business big data analysis and mathematical methods, and able to use them.
Abilities to carry out research
B1. Have ability methodically to justify, to plan, to organize and to perform analysis of business big data.
B2. Have ability to integrate knowledge of business, informatics and various mathematical modelling methods.
B3. 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.
B4. 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.
B5. Have ability to prepare reports and analytical insights, to communicate knowledge and understanding for managers, who make business decisions.
Special skills
C1. Have ability to think logically and analytically.
C2. 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.
C3. Have ability to abstract business information, to describe processes of business systems using mathematical relationships.
C4. Have ability to deal with non-standard complex business tasks in new and unfamiliar environments combining mathematics, computer science and business knowledge.
C5. Have ability to compare several solutions for the same issue and to find the optimum way according to selected criteria.
C6. Have ability to develop algorithms and computer programs for created models realization, to work with big data.
Social skills
D1. Have ability to critically evaluate their own and others performance and professional experience.
D2. Have ability to work independently and in the interdisciplinary team, to generate new ideas; to integrate knowledge and skills.
D3. Have ability clearly and reasonably to present information for specialists of other disciplines, have good communication skills.
Personal skills
E1. Have ability to make independently decisions, to considering their consequences and their complexity.
E2. Have ability to take responsibility for the results and quality.
E3. Have ability to choose the direction of enhancement and to develop acquired skills as needed.
Activities of teaching and learning:
Material of the study module is gained both by activity in auditorium and individual work. Activity in auditorium includes lectures, group research and practical work. Individual work is comprised by study of theoretical material, preparation for lectures, group research and practical work, midterms and exam. It also includes preparation of homework, projects and other activities.
Study programme is accomplished by final degree project.
Methods of assessment of learning achievements:
Knowledge, abilities and skills, that the student will acquire during the course of the programme, are graded and registered to databases twice: during the evaluation of the student‘s independent work (positive or negative grade) and during the exam session (grade in ten-point scale).
The final degree project is defended in the public session of the studies’ branch qualification committee.
Framework:
Study subjects (modules), practical training:
The future graduates study: Matrix analysis, Multidimensional statistical analysis models; Strategic business analysis; Big data mining methods; Optimization and business decision-making; Business risk and uncertainty analytics and other subjects. In total they study 14 subjects. The practice is not provided in the study programme.
Specialisations:
-
Optional courses:
During 1-3 semester students may have 21 credits choosing modules from the list: Financial and accounting data analytics; Marketing solutions modelling; Business logistics analysis; Business process modelling; Financial management solutions; Meta data analysis and information portals; Consumer behaviour research; Big data analytics tools; Time series analysis and forecasting; Financial mathematics models; Fractal analysis methods.
Distinctive features of a study programme:
The interdisciplinary programme designed to prepare highly qualified professionals which are able to assess and solve complex business problems.

Reference Data

Location:
Education Level:
Thematic area:
Language:
Teaching Language:
Study Type:
Duration:
Access requirements: 

Minimum education - Higher education university type

Credits: 

120

Provider Information

Provider Name: 
Kaunas University of Technology
Provider Type: 
University
Provider Contact Info: 

Provider phone number: +370-37-300000 Provider email: ktu@ktu.lt Provider URL: http://ktu.edu/

Qualifications Awarded

Reference Data

Qualification Awarded:
Master of Mathematics
Awarding body:

Kaunas University of Technology

Awarding body contact info:

Provider phone number: +370-37-300000 Provider email: ktu@ktu.lt Provider URL: http://ktu.edu/

Course Locations

Reference Data

Course address:

K. Donelaičio g. 73, Kaunas