Name of the programme
Data Science
Field of the programme
Information Technology (IT)
Language of the programme
English
Programme(s) and duration in semesters, number of contact hours
| Field of training | Number of semesters | Number of contact classes |
|---|---|---|
| part-time course | 4 |
Number of credits to be accumulated to obtain the degree
120 credit
Educational level and qualification indicated in the degree certificate
- educational level: magister (Master of Science, abbreviated: MSc)
- name of master course: Data Science
- qualification: Data Scientist
Aim of the course
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.
Main areas of the course
| Area | Credit |
|---|---|
| Basic theoretical knowledge | 15-25 |
| Practical basic knowledge | 15-25 |
| Specific knowledge | 40-60 |
| Optional subjects and Thesis | 30-50 |
| Total: | 120 |
Criteria requirements
Internship
Professional practice of at least 6 weeks (containing 240 work hours).
Verification of knowledge
a) during the semester with written or oral presentation, written test, or assessing home assignment (plan, measurement minutes etc.), with practical mark or signature;
b) passing a pre-examination during the semester;
c) passing an exam or a comprehensive exam in the exam period and
d) the final exam.
Conditions to take the final exam
a) abtaining the final certificate;
b) thesis approved by a reviewer.
Admission to the final examination is subject to the obtainment of a final certificate. The final certificate is issued to students having fulfilled all educational requirements specified in the curriculum – except for writing the thesis – and obtained the necessary amount of credits.
Components of the final exam
The final exam comprises the defence of the thesis and oral exams specified in the curriculum (with preparation times at least 30 minutes per subject), which have to be taken the same day.
Result of the final examination (F)
The overall result of the final examination is the average of grades obtained for the thesis and the subjects of the oral part of the final exam:
F =(Th+ S1+S2+…+Sm)/(1+m).
Conditions to issue the degree
a) successful final exams;
b) a complex language examination at level B2 or above in any foreign language.
