|Nominal duration||1.5 years (90 ECTS)|
|Awards||(Master in Mathematical Science)|
|Tuition fee||€5,200 per year|
|Application fee||€100 per year
The application fee is non-refundable.
Undergraduate diploma (or higher)
Basic knowledge of Calculus, Matrix Algebra, Probability Theory, Statistics, and Informatics (R, Python, or similar program) is required and can be checked during the interview.
Applicants may be asked to attend a motivational interview in order to evaluate their programme selection factors and English language skills. Motivational interviews are held remotely.
Important: additional country-specific requirements are set only for the following countries which you can find HERE (STEP 2)
The entry qualification documents are accepted in the following languages: English / Lithuanian.
Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.
Translation is considered to be official when it is bound together with the certified true copy of the original document and is confirmed by the translator’s signature. Documents issued in English, Lithuanian must be certified true copies and do not require translation.
IELTS 5.5+; iBT TOEFL 65+
If your first language is English and/or if you have a university degree in English, you will be exempt from providing an English language test score.
At least 2 reference(s) should be provided.
A motivation letter must be added to your application.
The aim of the programme is to educate internationally recognised professionals in Data Science who are experts in utilising the up-to-date knowledge of statistics, econometrics and data science in developing advanced mathematical (statistical) models for private and public institutions, for the purposes of planning, management, forecasting and evaluations of their activities.
The analytical modelling, planning, and forecasting work opportunities at various levels are open for Masters in Data Science in: research centres; financial institutions in private sector (e.g. pension funds, stock exchanges, insurance companies, commercial banks, Hi Tech start-ups); consulting firms; the analysis and planning units of business enterprises; central banks, ministries, and other public sector institutions.