Data Science

The aim is to train IT professionals who are able to understand the characteristics of different types of data and the structure of complex data sets, to recognise the relationships between them, to apply the necessary transformation steps to raw data, to draw conclusions and to model real-world processes. They are prepared to continue their studies at doctoral level.

The technical competences to be acquired

a) knowledge

Knowledge of the concepts of data analysis, ethics, data security, mathematics, statistics, programming principles and contexts, in particular data types, representations, transformation and optimisation procedures, multivariate statistics, machine learning, necessary for innovative, research-level data science. You will be familiar with the operation of current technologies used for analysis and modelling and be able to apply them in real-life situations, including for large amounts of data. Familiarity with techniques for storing, processing and visualising large amounts of data, and with the properties of the different tools. Knowledge of the main application areas of data science, the related problems and the main directions of possible solutions, and the limitations of the related techniques. Ability to make connections between different types of data, to extract information based on data transformation and to solve problems in a multidisciplinary environment. English language proficiency is sufficient for the level of training, knowledge of English-language literature, understanding and processing of technical texts and the performance of professional tasks for which the qualification is awarded, as well as for continuous professional development. Comprehensive knowledge of the regulatory issues and problems of data management, analysis and modelling, including legal and ethical aspects. Knowledge of IT security issues.

b) skills

Ability to formalise complex classification, modelling and forecasting problems in different disciplines, to define the necessary theoretical and practical methods and to solve them. Ability to construct the raw data transformation steps for the required task. Ability to link data with other information in context, combining different modalities. Knowledge of the dependencies between data elements and the structurability and types of data. Ability to adapt the data analysis strategy to changes in technology. Ability to collaborate effectively with data science related disciplines producing the data to be processed and analysed. Ability to effectively visualise large amounts of data and interpret results. Ability to generate original ideas beyond the identification and solution of routine problems. Ability to carry out independent scientific research under professional supervision. Ability to communicate, present and interpret results, prepare reports and process technical material in at least English and in addition to his/her mother tongue. In the course of his/her work, he/she shall explore the possibility of setting research, development and innovation objectives and shall strive to achieve them.

c) attitude

It monitors the latest developments in data science and related disciplines, in particular in mathematics, statistics, computer science and artificial intelligence, and seeks to use them for its own development. He respects and takes into account in his work the views of those who differ from his own and considers that only professional arguments are acceptable. Represent his/her profession credibly and present the results of his/her work. He/she is committed to promoting and implementing environmentally responsible behaviour.

d) their autonomy and responsibility

He/she pays great attention to carrying out his or her tasks with precision and to meeting and enforcing deadlines. Ability to work individually, as part of a team or as a leader on analytical, modelling and other data science tasks. Assume responsibility for the work of others working with or under him/her. Manage in a responsible manner, in accordance with current regulations, sensitive and potentially confidential data entrusted to him/her. Carry out his/her work with the utmost respect for professional and scientific ethics.