Data Science is the science of methods for analysing data and extracting valuable information and knowledge from it. It closely overlaps with such fields as Machine Learning and Cognitive Science and, of course, technologies for working with big data (Big Data).
Nowadays, major players in the economic world are becoming increasingly aware of the potential of their data. They are constantly looking for ways to utilise their data and extract as much useful information from it as possible. The role of data scientists is to help companies in this task by acquiring, storing, organising and processing this mass of information to extract value. A data science and artificial intelligence professional must have interdisciplinary skills ranging from a deep knowledge of maths and statistics to mastering the IT tools and infrastructure required to manage and process data. In addition, research scientists should have curiosity and a thirst to understand the application area in which they are working.
PhD Data Science should:
- complete 1 course of theoretical training;
- pass all the necessary exams to prove the mastery of theoretical knowledge;
- conduct research work for 3 years.
In the course of doctoral studies PhD students can realise all opportunities to engage in scientific activities, in particular have:
- access to library resources and electronic catalogues;
- the opportunity to consult with their supervisors, other professors;
- the opportunity to communicate and consult with leading scientists from many foreign universities;
- the possibility of foreign internships.
Aims and objectives of the educational programme
- Aims and objectives of the educational programme
- The aim of the educational programme is to prepare PhD doctors to become leaders in the field of data science research and to help them develop the research skills necessary for a career in academia or industry.
- Objectives of the educational programme:
- Develop a deep understanding of key technologies in data science and business intelligence: data mining, machine learning, visualisation techniques, predictive modelling and statistics.
- Practise problem analysis and decision making.
- Gain hands-on experience with statistical programming languages and big data tools through research papers.
- Gain skills in working with neural networks.