Study Plan

Competences

The competence framework states the academic capabilities this programme is designed to build. Each entry keeps its linked learning outcomes visible so the structure can be followed without leaving the page.

Key competences

4 entries

CC1

mathematical competence and competence in science, technology and engineering

See details

Linked learning outcomes

(no linked learning outcomes)

CC2

digital competence

See details

Linked learning outcomes

(no linked learning outcomes)

CC3

personal, social and learning to learn competence

See details

Linked learning outcomes

(no linked learning outcomes)

CC4

entrepreneurship competence

See details

Linked learning outcomes

(no linked learning outcomes)

Professional competences

26 entries

CP1

analyse business requirements

See details

Study clients' needs and expectations for a product or service in order to identify and resolve inconsistencies and possible disagreements of involved stakeholders.

Linked learning outcomes

C1 A1 A2 R1

CP2

apply ICT systems theory

See details

Implement principles of ICT systems theory in order to explain and document system characteristics that can be applied universally to other systems

Linked learning outcomes

C2 C3 A3 R2

CP3

conduct literature research

See details

Conduct a comprehensive and systematic research of information and publications on a specific literature topic. Present a comparative evaluative literature summary.

Linked learning outcomes

C4 C5 A4 R3

CP4

create data sets

See details

Generate a collection of new or existing related data sets that are made up out of separate elements but can be manipulated as one unit.

Linked learning outcomes

A5 A6 A7 A8

CP5

creatively use digital technologies

See details

Use digital tools and technologies to create knowledge and to innovate processes and products. Engage individually and collectively in cognitive processing to understand and resolve conceptual problems and problem situations in digital environments.

Linked learning outcomes

C6 A9 A10 R4

CP6

define technical requirements

See details

Specify technical properties of goods, materials, methods, processes, services, systems, software and functionalities by identifying and responding to the particular needs that are to be satisfied according to customer requirements.

Linked learning outcomes

C7 A11 A12 R5

CP7

deliver visual presentation of data

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Create visual representations of data such as charts or diagrams for easier understanding.

Linked learning outcomes

C8 A13 A14 R6

CP8

design database in the cloud

See details

Apply design principles for an adaptive, elastic, automated, loosely coupled databases making use of cloud infrastructure. Aim to remove any single point of failure through distributed database design.

Linked learning outcomes

C9 A15 A16 A17

CP9

design process

See details

Identify the workflow and resource requirements for a particular process, using a variety of tools such as process simulation software, flowcharting and scale models.

Linked learning outcomes

C10 C11 A18 A19

CP10

develop computer vision system

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Apply and combine different computer vision tools and methods such as image acquisition, image processing, image segmentation and classification, detection, etc. in one system to allow computers to extract information from digital images such as photographs or video.

Linked learning outcomes

C12 C13 A20 R7

CP11

develop data processing applications

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Create a customised software for processing data by selecting and using the appropriate computer programming language in order for an ICT system to produce demanded output based on expected input.

Linked learning outcomes

C14 C15 A21 R8

CP12

develop software prototype

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Create a first incomplete or preliminary version of a piece of software application to simulate some specific aspects of the final product.

Linked learning outcomes

C16 A22 A23 R9

CP13

develop statistical software

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Participate in the various development stages of computer programs for econometric and statistical analysis, such as research, new product development, prototyping, and maintenance.

Linked learning outcomes

C17 C18 A24 A25

CP14

establish data processes

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Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.

Linked learning outcomes

C19 A26 A27 R10

CP15

handle data samples

See details

Collect and select a set of data from a population by a statistical or other defined procedure.

Linked learning outcomes

C20 A28 A29 A30

CP16

implement data quality processes

See details

Apply quality analysis, validation and verification techniques on data to check data quality integrity.

Linked learning outcomes

C21 A31 A32 A33

CP17

manage data

See details

Administer all types of data resources through their lifecycle by performing data profiling, parsing, standardisation, identity resolution, cleansing, enhancement and auditing. Ensure the data is fit for purpose, using specialised ICT tools to fulfil the data quality criteria.

Linked learning outcomes

C22 A34 A35 R11

CP18

manage ICT data architecture

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Oversee regulations and use ICT techniques to define the information systems architecture and to control data gathering, storing, consolidation, arrangement and usage in an organisation.

Linked learning outcomes

C23 A36 A37 R12

CP19

normalise data

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Reduce data to their accurate core form (normal forms) in order to achieve such results as minimisation of dependency, elimination of redundancy, increase of consistency.

Linked learning outcomes

C24 A38 A39 R13

CP20

perform data cleansing

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Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.

Linked learning outcomes

C25 A40 A41 R14

CP21

perform dimensionality reduction

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Reduce the number of variables or features for a dataset in machine learning algorithms through methods such as principal component analysis, matrix factorization, autoencoder methods, and others.

Linked learning outcomes

C26 A42 A43 R15

CP22

process data

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Enter information into a data storage and data retrieval system via processes such as scanning, manual keying or electronic data transfer in order to process large amounts of data.

Linked learning outcomes

A44 A45 A46 R16

CP23

store digital data and systems

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Use software tools to archive data by copying and backing them up, in order to ensure their integrity and to prevent data loss.

Linked learning outcomes

C27 A47 A48 R17

CP24

use databases

See details

Use software tools for managing and organising data in a structured environment which consists of attributes, tables and relationships in order to query and modify the stored data.

Linked learning outcomes

C28 A49 A50 R18

CP25

use software libraries

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Utilise collections of codes and software packages which capture frequently used routines to help programmers simplify their work.

Linked learning outcomes

C29 A51 A52 A53

CP26

utilise computer-aided software engineering tools

See details

Use software tools (CASE) to support the development lifecycle, design and implementation of software and applications of high-quality that can be easily maintained.

Linked learning outcomes

C30 A54 A55 R19

Transversal personal competences

10 entries

CT1

analyse big data

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Collect and evaluate numerical data in large quantities, especially for the purpose of identifying patterns between the data.

Linked learning outcomes

C31 C32 A56 A57

CT2

apply statistical analysis techniques

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Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.

Linked learning outcomes

C33 C34 A58 R20

CT3

develop creative ideas

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Developing new artistic concepts and creative ideas.

Linked learning outcomes

C35 A59 A60 A61

CT4

execute analytical mathematical calculations

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Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.

Linked learning outcomes

C36 C37 A62 R21

CT5

implement data warehousing techniques

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Apply models and tools such as online analytical processing (OLAP) and Online transaction processing (OLTP), to integrate structured or unstructured data from sources, in order to create a central depository of historical and current data.

Linked learning outcomes

A63 A64 A65 A66

CT6

interpret current data

See details

Analyse data gathered from sources such as market data, scientific papers, customer requirements and questionnaires which are current and up-to-date in order to assess development and innovation in areas of expertise.

Linked learning outcomes

C38 A67 A68 R22

CT7

manage data collection systems

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Develop and manage methods and strategies used to maximise data quality and statistical efficiency in the collection of data, in order to ensure the gathered data are optimised for further processing.

Linked learning outcomes

C39 C40 A69 R23

CT8

manage quantitative data

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Gather, process and present quantitative data. Use the appropriate programs and methods for validating, organising and interpreting data.

Linked learning outcomes

A70 A71 A72 A73

CT9

use data processing techniques

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Gather, process and analyse relevant data and information, properly store and update data and represent figures and data using charts and statistical diagrams.

Linked learning outcomes

C41 A74 A75 R24

CT10

manage research data

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Produce and analyse scientific data originating from qualitative and quantitative research methods. Store and maintain the data in research databases. Support the re-use of scientific data and be familiar with open data management principles.

Linked learning outcomes

C42 A76 A77 R25

Transversal interpersonal competences

1 entries

CT11

report analysis results

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Produce research documents or give presentations to report the results of a conducted research and analysis project, indicating the analysis procedures and methods which led to the results, as well as potential interpretations of the results.

Linked learning outcomes

C43 C44 A78 A79

Transversal global citizenship competences

1 entries

CT12

identify processes for re-engineering

See details

Identify potential to restructure a company or part of its operations, e.g. by exploiting information technology.

Linked learning outcomes

C45 C46 A80 R26

Learning outcomes

Learning outcomes translate the competence framework into observable academic results. Each entry keeps its connected competences and subject anchors visible so the curriculum can be followed in both directions.

Knowledge

46 entries

Identify stakeholder requirements conflicts in business briefs.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP1

Linked subjects

(no linked subjects)

Identify ICT system components and their functional relationships.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP2

Linked subjects

Operating Systems I

Classify ICT system characteristics by functional purpose.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP2

Linked subjects

(no linked subjects)

Select relevant scholarly sources for an AI research topic.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP3

Linked subjects

(no linked subjects)

Compare findings across scientific publications on an AI topic.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP3

Linked subjects

(no linked subjects)

Evaluate digital workflows against task constraints.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP5

Linked subjects

(no linked subjects)

Translate stakeholder needs into measurable technical specifications.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CP6

Linked subjects

(no linked subjects)

Label chart elements for clear data interpretation.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP7

Linked subjects

(no linked subjects)

Assess database failure points in cloud deployment architectures.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP8

Linked subjects

Distributed Systems

C10

Identify workflow steps for a process scenario.

1 competences | 1 subjects

understand · 2/6

See details

Linked competences

CP9

Linked subjects

Programming I

C11

Map resource needs for a process model.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP9

Linked subjects

Programming I

C12

Select suitable image acquisition methods for a vision task.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP10

Linked subjects

(no linked subjects)

C13

Evaluate segmentation results against task criteria.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP10

Linked subjects

(no linked subjects)

C14

Classify input data requirements for processing workflows.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP11

Linked subjects

Programming I

C15

Select a programming language for a data processing task.

1 competences | 2 subjects

apply · 3/6

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Linked competences

CP11

Linked subjects

Programming I Advanced Logical and Functional Programming

C16

Select core features for a software prototype scope.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP12

Linked subjects

Programming I Programming II

C17

Classify statistical analysis requirements by project priority.

1 competences | 0 subjects

analyze · 4/6

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Linked competences

CP13

Linked subjects

(no linked subjects)

C18

Evaluate statistical software results against expected outputs.

1 competences | 0 subjects

evaluate · 6/6

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Linked competences

CP13

Linked subjects

(no linked subjects)

C19

Classify data types for algorithmic processing.

1 competences | 0 subjects

apply · 3/6

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Linked competences

CP14

Linked subjects

(no linked subjects)

C20

Document sample selection procedures for data datasets.

1 competences | 0 subjects

apply · 3/6

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Linked competences

CP15

Linked subjects

(no linked subjects)

C21

Document data quality defects in validation reports.

1 competences | 0 subjects

apply · 3/6

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Linked competences

CP16

Linked subjects

(no linked subjects)

C22

Classify data sources by quality risk.

1 competences | 1 subjects

analyze · 4/6

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Linked competences

CP17

Linked subjects

Data Mining

C23

Classify organisational data sources by governance and retention requirements.

1 competences | 2 subjects

analyze · 4/6

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Linked competences

CP18

Linked subjects

Data Mining Research Ethics

C24

Identify functional dependencies in a table schema.

1 competences | 0 subjects

analyze · 4/6

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Linked competences

CP19

Linked subjects

(no linked subjects)

C25

Identify corrupt records within structured datasets.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP20

Linked subjects

(no linked subjects)

C26

Interpret reduced feature representations.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP21

Linked subjects

Computer Vision Text Mining

C27

Classify backup copies by retention period.

1 competences | 0 subjects

analyze · 4/6

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Linked competences

CP23

Linked subjects

(no linked subjects)

C28

Normalize database tables to reduce redundancy.

1 competences | 1 subjects

analyze · 4/6

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Linked competences

CP24

Linked subjects

Data Warehouses

C29

Evaluate library output against expected program behavior.

1 competences | 0 subjects

evaluate · 6/6

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Linked competences

CP25

Linked subjects

(no linked subjects)

C30

Select suitable CASE tools for a software project.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP26

Linked subjects

(no linked subjects)

C31

Identify relevant variables in large data sets for pattern analysis.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CT1

Linked subjects

Data Mining

C32

Interpret summary statistics for trend assessment.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CT1

Linked subjects

(no linked subjects)

C33

Compare data distributions against expected model assumptions.

1 competences | 3 subjects

analyze · 4/6

See details

Linked competences

CT2

Linked subjects

Dynamical Systems In Machine Learning Advanced Neuroscience Machine Learning

C34

Interpret correlation results for variable relationships.

1 competences | 0 subjects

analyze · 4/6

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Linked competences

CT2

Linked subjects

(no linked subjects)

C35

Evaluate concept originality against project goals.

1 competences | 1 subjects

evaluate · 6/6

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Linked competences

CT3

Linked subjects

Computational Game Theory

C36

Calculate matrix operations for model training verification.

1 competences | 3 subjects

apply · 3/6

See details

Linked competences

CT4

Linked subjects

Architectures For Parallel Computing Parallel Computing Introduction To Quantum Computing

C37

Derive numerical estimates for optimization parameters.

1 competences | 4 subjects

analyze · 4/6

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Linked competences

CT4

Linked subjects

Computational Game Theory Introduction To Quantum Computing Operations Research and Optimization Metaheuristic Algorithms

C38

Classify current sources by relevance to project decisions.

1 competences | 3 subjects

analyze · 4/6

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Linked competences

CT6

Linked subjects

Multi-agent Systems Msc Thesis Preparation Scientific Seminar

C39

Design data collection workflows for balanced sample acquisition.

1 competences | 0 subjects

create · 5/6

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Linked competences

CT7

Linked subjects

(no linked subjects)

C40

Evaluate data collection outputs against quality criteria.

1 competences | 0 subjects

evaluate · 6/6

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Linked competences

CT7

Linked subjects

(no linked subjects)

C41

Classify datasets by relevance to project objectives.

1 competences | 0 subjects

analyze · 4/6

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Linked competences

CT9

Linked subjects

(no linked subjects)

C42

Classify research datasets by method, source, and sensitivity.

1 competences | 1 subjects

analyze · 4/6

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Linked competences

CT10

Linked subjects

Big Data Technologies

C43

Structure research findings into a concise technical report.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CT11

Linked subjects

(no linked subjects)

C44

Explain analysis methods used in a research project.

1 competences | 0 subjects

understand · 2/6

See details

Linked competences

CT11

Linked subjects

(no linked subjects)

C45

Assess operational workflows for restructuring opportunities.

1 competences | 3 subjects

analyze · 4/6

See details

Linked competences

CT12

Linked subjects

Operations Research and Optimization Big Data Technologies Robotic Process Automation

C46

Identify process dependencies in operational workflows.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CT12

Linked subjects

Distributed Systems Robotic Process Automation

Skills

80 entries

Prioritize stakeholder requirements by project constraints.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP1

Linked subjects

(no linked subjects)

Document stakeholder requirements in a structured specification.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP1

Linked subjects

(no linked subjects)

Explain ICT system boundaries using system interaction evidence.

1 competences | 4 subjects

understand · 2/6

See details

Linked competences

CP2

Linked subjects

Operating Systems I Architectures For Parallel Computing Parallel Computing Modeling and Verifying Algorithms In Coq

Assess publication credibility for an AI research topic.

1 competences | 0 subjects

evaluate · 6/6

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Linked competences

CP3

Linked subjects

(no linked subjects)

Compile datasets from distributed source files into one structured collection.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP4

Linked subjects

(no linked subjects)

Merge related records into a unified dataset schema.

1 competences | 0 subjects

apply · 3/6

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Linked competences

CP4

Linked subjects

(no linked subjects)

Organize data elements into a coherent training dataset.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP4

Linked subjects

(no linked subjects)

Assemble labeled samples into a single evaluation dataset.

1 competences | 0 subjects

apply · 3/6

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Linked competences

CP4

Linked subjects

(no linked subjects)

Select digital tools for solving project-specific conceptual problems.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP5

Linked subjects

Multi-agent Systems

A10

Prototype digital solutions for process improvement tasks.

1 competences | 2 subjects

create · 5/6

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Linked competences

CP5

Linked subjects

Multi-agent Systems Penetration Testing

A11

Prioritize technical requirements against project constraints.

1 competences | 0 subjects

evaluate · 6/6

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Linked competences

CP6

Linked subjects

(no linked subjects)

A12

Validate technical requirements against stakeholder needs.

1 competences | 0 subjects

evaluate · 6/6

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Linked competences

CP6

Linked subjects

(no linked subjects)

A13

Select appropriate chart types for dataset characteristics.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP7

Linked subjects

(no linked subjects)

A14

Format visual hierarchy for presentation slides.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP7

Linked subjects

(no linked subjects)

A15

Design cloud database schemas for distributed deployment resilience.

1 competences | 1 subjects

create · 5/6

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Linked competences

CP8

Linked subjects

Distributed Systems

A16

Select cloud database services against availability and scaling requirements.

1 competences | 1 subjects

evaluate · 6/6

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Linked competences

CP8

Linked subjects

Distributed Systems

A17

Configure cloud database replication for automated failover handling.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP8

Linked subjects

Distributed Systems

A18

Select a suitable process simulation tool.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP9

Linked subjects

(no linked subjects)

A19

Construct a flowchart for a process scenario.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CP9

Linked subjects

(no linked subjects)

A20

Preprocess image data for downstream vision models.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP10

Linked subjects

(no linked subjects)

A21

Implement a data transformation routine from specified input.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP11

Linked subjects

Programming I Advanced Logical and Functional Programming

A22

Build a software prototype from defined functional requirements.

1 competences | 2 subjects

create · 5/6

See details

Linked competences

CP12

Linked subjects

Programming I Programming II

A23

Implement interface elements in a software prototype.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP12

Linked subjects

(no linked subjects)

A24

Design a statistical model for a defined analysis goal.

1 competences | 2 subjects

create · 5/6

See details

Linked competences

CP13

Linked subjects

Operations Research and Optimization Metaheuristic Algorithms

A25

Prototype a statistical algorithm in working code.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP13

Linked subjects

(no linked subjects)

A26

Prepare datasets for machine learning workflows.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP14

Linked subjects

(no linked subjects)

A27

Transform raw data into structured features.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP14

Linked subjects

(no linked subjects)

A28

Select representative data samples for training datasets.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP15

Linked subjects

(no linked subjects)

A29

Evaluate sample quality against dataset criteria.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP15

Linked subjects

(no linked subjects)

A30

Verify sample representativeness against population characteristics.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP15

Linked subjects

(no linked subjects)

A31

Validate dataset records against predefined quality rules.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP16

Linked subjects

(no linked subjects)

A32

Detect missing values in structured datasets.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP16

Linked subjects

(no linked subjects)

A33

Assess dataset consistency across related fields.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP16

Linked subjects

(no linked subjects)

A34

Profile data sets for completeness anomalies.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP17

Linked subjects

Data Mining

A35

Validate standardized records against reference rules.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP17

Linked subjects

Data Mining

A36

Define data storage structures for organisational information systems.

1 competences | 1 subjects

create · 5/6

See details

Linked competences

CP18

Linked subjects

Data Warehouses

A37

Evaluate data consolidation rules against system architecture constraints.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP18

Linked subjects

Data Mining Data Warehouses

A38

Normalize relational tables to third normal form.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP19

Linked subjects

(no linked subjects)

A39

Detect redundancy in relational data structures.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP19

Linked subjects

(no linked subjects)

A40

Validate record structure against dataset guidelines.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP20

Linked subjects

Programming I

A41

Correct invalid fields in dataset records.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP20

Linked subjects

Programming I

A42

Select the reduction method for a dataset.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP21

Linked subjects

Computer Vision

A43

Prepare features for dimensionality reduction.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP21

Linked subjects

Computer Vision

A44

Verify records against source documents before system entry.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP22

Linked subjects

(no linked subjects)

A45

Enter records into a database with consistent field formats.

1 competences | 1 subjects

precision · 3/5

See details

Linked competences

CP22

Linked subjects

Data Warehouses

A46

Correct data entry errors in stored records.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP22

Linked subjects

Data Warehouses

A47

Verify archive integrity after backup creation.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP23

Linked subjects

Practice Stage (112h)

A48

Restore archived data from backup copies.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP23

Linked subjects

Practice Stage (112h)

A49

Create relational tables for a defined data model.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP24

Linked subjects

Data Warehouses

A50

Construct SQL queries for requested data retrieval.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP24

Linked subjects

Data Warehouses

A51

Select suitable library functions for a given programming task.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP25

Linked subjects

(no linked subjects)

A52

Integrate a library into an application build configuration.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP25

Linked subjects

(no linked subjects)

A53

Invoke library methods with correct parameters.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP25

Linked subjects

Programming II

A54

Configure CASE tool settings for project workflows.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP26

Linked subjects

(no linked subjects)

A55

Model software structure with CASE diagrams.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP26

Linked subjects

(no linked subjects)

A56

Clean numerical data before statistical evaluation.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT1

Linked subjects

Data Mining

A57

Compare data distributions across large samples for anomalies.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CT1

Linked subjects

Data Mining

A58

Calculate descriptive statistics for structured datasets.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CT2

Linked subjects

(no linked subjects)

A59

Generate original concept variants for AI solution features.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CT3

Linked subjects

(no linked subjects)

A60

Combine diverse source ideas into novel visual concepts.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CT3

Linked subjects

(no linked subjects)

A61

Refine concept sketches into implementable design directions.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CT3

Linked subjects

(no linked subjects)

A62

Compute statistical measures for experiment results.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT4

Linked subjects

Computational Game Theory

A63

Design star schemas for analytical data storage.

1 competences | 1 subjects

create · 5/6

See details

Linked competences

CT5

Linked subjects

Data Warehouses

A64

Map source data into warehouse staging tables.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT5

Linked subjects

Data Warehouses

A65

Validate warehouse loads against source records.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CT5

Linked subjects

Data Warehouses

A66

Optimize OLAP queries for analytical performance.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT5

Linked subjects

Data Warehouses

A67

Extract decision signals from current research findings.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CT6

Linked subjects

Multi-agent Systems Msc Thesis Preparation

A68

Prioritize current evidence by project relevance.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CT6

Linked subjects

Msc Thesis Preparation Scientific Seminar

A69

Configure collection settings to reduce missing data.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CT7

Linked subjects

(no linked subjects)

A70

Validate dataset quality before loading it into analysis workflows.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT8

Linked subjects

Data Mining

A71

Structure quantitative data into analysis-ready tables.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CT8

Linked subjects

(no linked subjects)

A72

Interpret data patterns to support reporting decisions.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CT8

Linked subjects

(no linked subjects)

A73

Present quantitative findings in clear visual summaries.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CT8

Linked subjects

(no linked subjects)

A74

Validate data quality against project requirements.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CT9

Linked subjects

(no linked subjects)

A75

Present statistical results in appropriate charts.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CT9

Linked subjects

(no linked subjects)

A76

Validate research data entries against collection records.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CT10

Linked subjects

Big Data Technologies

A77

Store research data in a database schema.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT10

Linked subjects

Big Data Technologies

A78

Present analysis results to a technical audience.

1 competences | 0 subjects

responding · 2/5

See details

Linked competences

CT11

Linked subjects

(no linked subjects)

A79

Interpret research results against project hypotheses.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CT11

Linked subjects

(no linked subjects)

A80

Evaluate process bottlenecks against technology capabilities.

1 competences | 3 subjects

evaluate · 6/6

See details

Linked competences

CT12

Linked subjects

Operations Research and Optimization Big Data Technologies Robotic Process Automation

Responsibility

26 entries

Resolve stakeholder requirement disagreements through evidence-based comparison.

1 competences | 0 subjects

responding · 2/5

See details

Linked competences

CP1

Linked subjects

(no linked subjects)

Document ICT system behavior using operational observations.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP2

Linked subjects

Operating Systems I

Compose a structured literature summary for an AI topic.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CP3

Linked subjects

(no linked subjects)

Adapt digital methods to resolve technical ambiguities.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP5

Linked subjects

(no linked subjects)

Document technical requirements in a structured specification.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CP6

Linked subjects

(no linked subjects)

Evaluate chart clarity against presentation goals.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP7

Linked subjects

(no linked subjects)

Configure detection parameters for image analysis tasks.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP10

Linked subjects

(no linked subjects)

Evaluate program output against expected input conditions.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP11

Linked subjects

Programming I

Assess prototype behavior against intended use cases.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP12

Linked subjects

(no linked subjects)

R10

Evaluate data quality against processing requirements.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP14

Linked subjects

(no linked subjects)

R11

Audit identity matches for duplicate records.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP17

Linked subjects

Data Warehouses

R12

Monitor data usage controls against organisational access policies.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP18

Linked subjects

Research Ethics

R13

Verify schema consistency after normalization changes.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP19

Linked subjects

(no linked subjects)

R14

Verify dataset integrity after cleansing actions.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP20

Linked subjects

(no linked subjects)

R15

Evaluate retained information after reduction.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP21

Linked subjects

(no linked subjects)

R16

Track submitted records for completeness before approval.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP22

Linked subjects

Practice Stage (112h)

R17

Schedule routine backups for digital datasets.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP23

Linked subjects

Practice Stage (112h)

R18

Update database records within transaction boundaries.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP24

Linked subjects

Data Warehouses

R19

Evaluate CASE outputs for maintenance quality.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP26

Linked subjects

(no linked subjects)

R20

Assess machine learning outputs against validation data.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CT2

Linked subjects

Dynamical Systems In Machine Learning Machine Learning

R21

Validate calculation outputs against problem constraints.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CT4

Linked subjects

Operations Research and Optimization

R22

Compare current customer needs against available evidence.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CT6

Linked subjects

Multi-agent Systems Msc Thesis Preparation

R23

Monitor collection system performance for sampling bias.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CT7

Linked subjects

(no linked subjects)

R24

Store datasets using approved organisational procedures.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CT9

Linked subjects

(no linked subjects)

R25

Apply open data principles to research dataset publication.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT10

Linked subjects

Big Data Technologies

R26

Prioritize restructuring options by business impact.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CT12

Linked subjects

Computational Game Theory Operations Research and Optimization

Curriculum

The semester structure keeps the curriculum grouped by mandatory, elective, and optional subjects. Expand a row to inspect hours, subject type, linked learning outcomes, and any public syllabus.

Semester 1

Credits: 30 | Total hours/week: 16

Mandatory subjects

Fundamentals Of Artificial Intelligence

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

(no linked learning outcomes)

Computer Vision

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

C26 A42 A43

Introduction To Robotics

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

(no linked learning outcomes)

Research Ethics

2 credits

Lecture 1 Seminar 0 Lab 0 Practical 0 Kind DC

C23 R12

Elective subjects

5 credits

Distributed Systems

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C9 C46 A15 A16 A17

Operations Research and Optimization

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C37 C45 A24 A80 R21 R26

Computational Geometry

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

5 credits

Dynamical Systems In Machine Learning

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C33 R20

Architectures For Parallel Computing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C36 A3

Metaheuristic Algorithms

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C37 A24

Optional subjects

Programming I

6 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DF

C10 C11 C14 C15 C16 A21 A22 A40 A41 R8

Volunteering I (60 H)

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)

Operating Systems I

5 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DF

C2 A3 R2

Semester 2

Credits: 30 | Total hours/week: 24

Mandatory subjects

Multi-agent Systems

5 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

C38 A9 A10 A67 R22

Practice Stage (112h)

5 credits

Lecture 0 Seminar 0 Lab 1 Practical 8 Kind DS

A47 A48 R16 R17

Robotic Process Automation

5 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

C45 C46 A80

Elective subjects

5 credits

Special Topics In Artificial Intelligence

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Data Mining

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C22 C23 C31 A34 A35 A37 A56 A57 A70

5 credits

Data Warehouses

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C28 A36 A37 A45 A46 A49 A50 A63 A64 A65 A66 R11 R18

Integrated Security Systems - Physical and Cyber Protection

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Parallel Computing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C36 A3

5 credits

Modeling and Verifying Algorithms In Coq

Lecture 1 Seminar 0 Lab 2 Practical 0 Kind DS

Academic and Technical Writing

Lecture 1 Seminar 0 Lab 2 Practical 0 Kind DS

(no linked learning outcomes)

Big Data Technologies

Lecture 1 Seminar 0 Lab 2 Practical 0 Kind DS

C42 C45 A76 A77 A80 R25

Optional subjects

Logic and Functional Programming

4 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DS

(no linked learning outcomes)

Programming II

6 credits

Lecture 2 Seminar 0 Lab 3 Practical 0 Kind DS

C16 A22 A53

Volunteering II (60 H)

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)

Semester 3

Credits: 30 | Total hours/week: 16

Mandatory subjects

Machine Learning

6 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DF

C33 R20

Advanced Neuroscience

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C33

Control Theory and Human Robotics Interaction

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Elective subjects

6 credits

Text Mining

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C26

Computational Game Theory

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C35 C37 A62 R26

Introduction To Quantum Computing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C36 C37

Penetration Testing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

A10

6 credits

Techniques For Scientific Work

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Algorithm Synthesis and Mathematical Theory Exploration

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Advanced Logical and Functional Programming

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C15 A21

Optional subjects

Intelligent Systems and Machine Learning

5 credits

Lecture 2 Seminar 0 Lab 0 Practical 2 Kind DC

(no linked learning outcomes)

Volunteering III (60 H)

2 credits

Lecture 0 Seminar 1 Lab 0 Practical 0 Kind DC

(no linked learning outcomes)

Semester 4

Credits: 30 | Total hours/week: 21

Mandatory subjects

Professional Practice

8 credits

Lecture 0 Seminar 0 Lab 2 Practical 12 Kind DS

(no linked learning outcomes)

Msc Thesis Preparation

15 credits

Lecture 0 Seminar 0 Lab 5 Practical 0 Kind DS

C38 A67 A68 R22

Scientific Seminar

7 credits

Lecture 0 Seminar 0 Lab 2 Practical 0 Kind DS

C38 A68

Elective subjects

(no elective groups)

Optional subjects

Volunteering IV (60 H)

2 credits

Lecture 0 Seminar 1 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)