
About the programme
| Faculty | Faculty of Mathematics and Informatics | Duration | 3 years |
| Study field group | Mathematical Sciences | Mode of study | Full-time |
| Study field | Statistics | Credits | 180 |
| Qualification degree | degree of Bachelor of Mathematical Sciences | Annual tuition fee | 4 500 EUR for non-EU students / 4 201 EUR for EU students |
* For applicants from non-EU/EFTA countries
** For applicants who do not need a Lithuanian visa and non-EU/EFTA applicants, if there is a Lithuanian embassy in their home country
Why this programme?
The Business Data Analytics programme is interdisciplinary, integrating the field of business, with one-third of the credits devoted to business and finance courses. This three-year programme allows students to enter the labour market more quickly, meeting the strong and growing demand for its graduates.
Designed to develop advanced analytical and problem-solving skills, the programme combines knowledge of statistics, machine learning, business, and finance. Students learn to collect, analyse, and interpret complex business data, apply statistical methods in finance and other uncertainty-related fields, critically evaluate information, communicate results effectively, and work both independently and as part of a team – continuously enhancing their professional growth.
What‘s after?
The graduates will have the opportunity to continue their studies in Master’s programmes in data analysis/statistics, data science, or econometrics at universities in Lithuania and abroad.
They will also be well-equipped to pursue careers in the business sector as business data analysts, data analysts, data analysis consultants, risk analysts, financial analysts, and other related professionals. Additionally, graduates may find employment in research or applied science centres specialising in economic data analysis.
Programme partners – VU Business School and University of Minho, School of Economics, Management and Political Science.
Programme specific requirements
- Foreigners are required to pass the OMPT-D exam with a score of at least 70%. Foreigners from the following countries are not required to take the test: EU countries, Iceland, Liechtenstein, Norway, Switzerland, the United Kingdom, Ukraine, Turkey, the USA, Israel, Singapore, Canada, Japan, South Korea, the Republic of South Africa, Australia, and New Zealand. The Admissions Committee evaluates the qualifications of applicants and invites them to a motivational interview only after receiving the OMPT-D exam results with a score of at least 70%.
- Assessment of motivation is held by electronic means remotely for all foreign applicants, and the final decision is made by the Admissions Committee only after the motivational interview.
Additional country-specific requirements are set only for the following countries which you can find HERE.
Programme structure
| Subject | Credits |
| Semester 1 Autumn | |
| Compulsory Courses | 30.0 |
| Fundamentals of Statistical Bussiness Data Analysis | 10.0 |
| Informatics for Data Analysis | 10.0 |
| Microeconomics | 5.0 |
| Scientific Computing I/II p. | 5.0 |
| Semester 2 Spring | |
| Compulsory Courses | 20.0 |
| Database Management Systems | 5.0 |
| Macroeconomics | 5.0 |
| Scientific Computing II/II p. | 10.0 |
| Course unit (module) of Individual Studies | 10.0 |
| Semester 3 Autumn | |
| Compulsory Courses | 20.0 |
| Business Process Management | 5.0 |
| Marketing | 5.0 |
| Statistics and Probability Theory | 10.0 |
| Course unit (module) of Individual Studies | 10.0 |
| Semester 4 Spring | |
| Compulsory Courses | 15.0 |
| Corporate Finance | 5.0 |
| Data Visualization | 5.0 |
| Practical Data Analysis with R and Python | 5.0 |
| Optional Courses | 5.0 |
| Financial Intelligence | 5.0 |
| Game Theory | 5.0 |
| Language Models | 5.0 |
| Sampling Methods | 5.0 |
| Course unit (module) of Individual Studies | 10.0 |
| Semester 5 Autumn | |
| Compulsory Courses | 20.0 |
| Big Data Software Tools | 5.0 |
| Business Data Analytics Project | 5.0 |
| Entrepreneurship | 5.0 |
| Time Series Models | 5.0 |
| Optional Courses | 10.0 |
| Data Tidying and Transformation with R | 5.0 |
| Machine Learning for Science and Engineering Studies | 5.0 |
| Numerical Methods | 5.0 |
| Optimization Methods | 5.0 |
| Semester 6 Spring | |
| Compulsory Courses | 30.0 |
| Bachelor Final Thesis (Study field: Statistics) | 15.0 |
| Professional Practice | 15.0 |