Bachelor’s degree

Data Analysis and Artificial Intelligence is an interdisciplinary study programme at the Faculty of Science at the Pavol Jozef Šafárik University in Košice, which combines the theoretical and practical skills from the field of mathematics and computer science in a balanced manner. Interdisciplinarity provides the graduate greater opportunities in choosing follow-up studies or on the labor market. For more info, see the faculty page.

Selection of courses:

1st year of study

  • Introduction to data analysis – The student will get an idea of what the focus and application of their studies will be right at the beginning.
  • Algebra – The level of mathematic skills is what distinguishes our graduates from users of various analytical tools.
  • Mathematical software – Many problems can no longer be solved without a computer. We will teach the student how to chose and use the right software.
  • User environments of operating systems – Using a computer is a necessary supposition to working with data.
  • Database systems for mathematicians – Data need to be stored and handled correctly.
  • Programming, algorithms and complexity – The student will acquire the ability to process data but also have the possibility to change their specification.
  • English Language of Natural Science – The English language is fundamental to the study programme Data Analysis and Artificial Intelligence.

2nd year of study

  • Function of Real variable, Linear and Integer programming, Discrete mathematics for informaticians, Furnction of Real variables – A further dive into exact foundations is needed after a warm-up.
  • Probability theory – Plenty of events around us is non-deterministic.
  • Introduction to Machine Learning – Machine Learning moves today’s world.
  • Introduction to Information Security – One has to be careful when dealing with data.
  • Automata and formal languages – Programming languages change while the foundations they are built on stay invariant.
  • Advanced programming in Python – We are keeping up with current trends in the field.
  • Project DA I – Practical experience in the data processing life cycle is invaluable

3rd year of study

  • Mathematical statistics – Ordinary methods of data analysis are still going strong.
  • Project DA II – There is no such thing as having enough of practical experience.
  • Numerical Methods – An exact answer to some problems does not always exist, but real life expects a solution.
  • Functional Programming, Concurrent programming – Each problem has to be analyzed from different angles.
  • Introduction to neural networks