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

21 entries

CP1

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

A1 A2 A3 A4

CP2

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

C18 A30 A31 A32

CP3

develop data processing applications

See details

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

C1 C2 A5 R1

CP4

establish data processes

See details

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

Linked learning outcomes

C3 A6 A7 R2

CP5

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

C4 A8 A9 A10

CP6

implement data quality processes

See details

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

Linked learning outcomes

C5 A11 A12 A13

CP7

integrate ICT data

See details

Combine data from sources to provide unified view of the set of these data.

Linked learning outcomes

C19 A33 A34 R11

CP8

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

C20 A35 A36 R12

CP9

manage ICT data architecture

See details

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

C21 A37 A38 R13

CP10

normalise data

See details

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

C6 A14 A15 R3

CP11

perform data cleansing

See details

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

Linked learning outcomes

C7 A16 A17 R4

CP12

perform data mining

See details

Explore large datasets to reveal patterns using statistics, database systems or artificial intelligence and present the information in a comprehensible way.

Linked learning outcomes

C22 A39 A40 A41

CP13

perform dimensionality reduction

See details

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

C8 A18 A19 R5

CP14

process data

See details

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

A42 A43 A44 R14

CP15

store digital data and systems

See details

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

C23 A45 A46 R15

CP16

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

C24 A47 A48 R16

CP17

implement data warehousing techniques

See details

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

A49 A50 A51 A52

CP18

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

C9 A20 A21 R6

CP19

execute analytical mathematical calculations

See details

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

Linked learning outcomes

C10 C11 A22 R7

CP20

apply statistical analysis techniques

See details

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

C12 C13 A23 R8

CP21

manage research data

See details

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

C14 A24 A25 R9

Transversal personal competences

5 entries

CT1

analyse big data

See details

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

Linked learning outcomes

C15 C16 A26 A27

CT2

collect ICT data

See details

Gather data by designing and applying search and sampling methods.

Linked learning outcomes

C25 A53 A54 R17

CT3

define data quality criteria

See details

Specify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.

Linked learning outcomes

A55 A56 A57 R18

CT4

manage quantitative data

See details

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

Linked learning outcomes

A58 A59 A60 A61

CT5

use data processing techniques

See details

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

C17 A28 A29 R10

Transversal interpersonal competences

1 entries

CT6

report analysis results

See details

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

C26 C27 A62 A63

Transversal global citizenship competences

1 entries

CT7

manage intellectual property rights

See details

Deal with the private legal rights that protect the products of the intellect from unlawful infringement.

Linked learning outcomes

C28 A64 A65 R19

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

28 entries

Classify input data requirements for processing workflows.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP3

Linked subjects

Programming I

Select a programming language for a data processing task.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP3

Linked subjects

Programming I Advanced Logic and Functional Programming

Classify data types for algorithmic processing.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP4

Linked subjects

(no linked subjects)

Document sample selection procedures for data datasets.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP5

Linked subjects

Biostatistics and Medical Data Analysis

Document data quality defects in validation reports.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP6

Linked subjects

Databases

Identify functional dependencies in a table schema.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP10

Linked subjects

Databases

Identify corrupt records within structured datasets.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP11

Linked subjects

(no linked subjects)

Interpret reduced feature representations.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP13

Linked subjects

Computer Vision Text Mining

Classify current sources by relevance to project decisions.

1 competences | 3 subjects

analyze · 4/6

See details

Linked competences

CP18

Linked subjects

Multi-agent Systems Msc Thesis Preparation Scientific Seminar

C10

Calculate matrix operations for model training verification.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP19

Linked subjects

Parallel Computing Introduction To Quantum Computing

C11

Derive numerical estimates for optimization parameters.

1 competences | 3 subjects

analyze · 4/6

See details

Linked competences

CP19

Linked subjects

Introduction To Quantum Computing Operations Research and Optimization Metaheuristic Algorithms

C12

Compare data distributions against expected model assumptions.

1 competences | 5 subjects

analyze · 4/6

See details

Linked competences

CP20

Linked subjects

Biostatistics and Medical Data Analysis Dynamical Systems In Machine Learning Machine Learning Probabilistic Models For Data Science Statistical Methods For Clinical Studies

C13

Interpret correlation results for variable relationships.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP20

Linked subjects

Biostatistics and Medical Data Analysis Probabilistic Models For Data Science

C14

Classify research datasets by method, source, and sensitivity.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP21

Linked subjects

Big Data Technologies

C15

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

C16

Interpret summary statistics for trend assessment.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CT1

Linked subjects

(no linked subjects)

C17

Classify datasets by relevance to project objectives.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CT5

Linked subjects

(no linked subjects)

C18

Assess database failure points in cloud deployment architectures.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP2

Linked subjects

Distributed Systems

C19

Validate source schemas before combining records into a unified dataset.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP7

Linked subjects

Data Warehouses Distributed Methods and Technologies Based On Xml

C20

Classify data sources by quality risk.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP8

Linked subjects

Data Mining

C21

Classify organisational data sources by governance and retention requirements.

1 competences | 3 subjects

analyze · 4/6

See details

Linked competences

CP9

Linked subjects

Data Mining Research Ethics Professional Practice

C22

Evaluate data quality before mining analysis begins.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP12

Linked subjects

Data Mining

C23

Classify backup copies by retention period.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP15

Linked subjects

Professional Practice

C24

Normalize database tables to reduce redundancy.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP16

Linked subjects

Data Warehouses

C25

Select relevant data sources for a defined analytical question.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT2

Linked subjects

Data Mining

C26

Structure research findings into a concise technical report.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CT6

Linked subjects

(no linked subjects)

C27

Explain analysis methods used in a research project.

1 competences | 0 subjects

understand · 2/6

See details

Linked competences

CT6

Linked subjects

(no linked subjects)

C28

Assess patent relevance for AI model components.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CT7

Linked subjects

(no linked subjects)

Skills

65 entries

Compile datasets from distributed source files into one structured collection.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP1

Linked subjects

Databases

Merge related records into a unified dataset schema.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP1

Linked subjects

Databases

Organize data elements into a coherent training dataset.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP1

Linked subjects

Databases

Assemble labeled samples into a single evaluation dataset.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP1

Linked subjects

(no linked subjects)

Implement a data transformation routine from specified input.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP3

Linked subjects

Programming I Advanced Logic and Functional Programming

Prepare datasets for machine learning workflows.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP4

Linked subjects

(no linked subjects)

Transform raw data into structured features.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP4

Linked subjects

(no linked subjects)

Select representative data samples for training datasets.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP5

Linked subjects

Statistical Methods For Clinical Studies

Evaluate sample quality against dataset criteria.

1 competences | 0 subjects

analyze · 4/6

See details

Linked competences

CP5

Linked subjects

(no linked subjects)

A10

Verify sample representativeness against population characteristics.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP5

Linked subjects

Biostatistics and Medical Data Analysis Statistical Methods For Clinical Studies

A11

Validate dataset records against predefined quality rules.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP6

Linked subjects

Databases

A12

Detect missing values in structured datasets.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP6

Linked subjects

Databases

A13

Assess dataset consistency across related fields.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP6

Linked subjects

Databases

A14

Normalize relational tables to third normal form.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP10

Linked subjects

Databases

A15

Detect redundancy in relational data structures.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CP10

Linked subjects

Databases

A16

Validate record structure against dataset guidelines.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP11

Linked subjects

Programming I

A17

Correct invalid fields in dataset records.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP11

Linked subjects

Programming I

A18

Select the reduction method for a dataset.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP13

Linked subjects

Computer Vision

A19

Prepare features for dimensionality reduction.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP13

Linked subjects

Computer Vision

A20

Extract decision signals from current research findings.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP18

Linked subjects

Multi-agent Systems Msc Thesis Preparation

A21

Prioritize current evidence by project relevance.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP18

Linked subjects

Msc Thesis Preparation Scientific Seminar

A22

Compute statistical measures for experiment results.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CP19

Linked subjects

(no linked subjects)

A23

Calculate descriptive statistics for structured datasets.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP20

Linked subjects

Biostatistics and Medical Data Analysis Probabilistic Models For Data Science

A24

Validate research data entries against collection records.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP21

Linked subjects

Big Data Technologies

A25

Store research data in a database schema.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP21

Linked subjects

Big Data Technologies

A26

Clean numerical data before statistical evaluation.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT1

Linked subjects

Data Mining

A27

Compare data distributions across large samples for anomalies.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CT1

Linked subjects

Data Mining

A28

Validate data quality against project requirements.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CT5

Linked subjects

(no linked subjects)

A29

Present statistical results in appropriate charts.

1 competences | 0 subjects

create · 5/6

See details

Linked competences

CT5

Linked subjects

(no linked subjects)

A30

Design cloud database schemas for distributed deployment resilience.

1 competences | 1 subjects

create · 5/6

See details

Linked competences

CP2

Linked subjects

Distributed Systems

A31

Select cloud database services against availability and scaling requirements.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP2

Linked subjects

Distributed Systems

A32

Configure cloud database replication for automated failover handling.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP2

Linked subjects

Distributed Systems

A33

Map field values across datasets into a common representation.

1 competences | 3 subjects

apply · 3/6

See details

Linked competences

CP7

Linked subjects

Data Warehouses Distributed Methods and Technologies Based On Xml Special Topics In Artificial Intelligence

A34

Resolve duplicate records across integrated data sources.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP7

Linked subjects

Data Warehouses Distributed Methods and Technologies Based On Xml

A35

Profile data sets for completeness anomalies.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP8

Linked subjects

Data Mining

A36

Validate standardized records against reference rules.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP8

Linked subjects

Data Mining

A37

Define data storage structures for organisational information systems.

1 competences | 2 subjects

create · 5/6

See details

Linked competences

CP9

Linked subjects

Data Warehouses Distributed Methods and Technologies Based On Xml

A38

Evaluate data consolidation rules against system architecture constraints.

1 competences | 3 subjects

evaluate · 6/6

See details

Linked competences

CP9

Linked subjects

Data Mining Data Warehouses Distributed Methods and Technologies Based On Xml

A39

Identify data patterns from large datasets using statistical methods.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP12

Linked subjects

Text Mining Data Mining

A40

Query datasets from database systems for mining analysis.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP12

Linked subjects

Text Mining Data Mining

A41

Present mining findings in a clear visual format.

1 competences | 1 subjects

create · 5/6

See details

Linked competences

CP12

Linked subjects

Data Mining

A42

Verify records against source documents before system entry.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP14

Linked subjects

Professional Practice Big Data Applications

A43

Enter records into a database with consistent field formats.

1 competences | 2 subjects

precision · 3/5

See details

Linked competences

CP14

Linked subjects

Data Warehouses Professional Practice

A44

Correct data entry errors in stored records.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP14

Linked subjects

Data Warehouses Professional Practice

A45

Verify archive integrity after backup creation.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP15

Linked subjects

Professional Practice Practice Stage (112h)

A46

Restore archived data from backup copies.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP15

Linked subjects

Professional Practice Practice Stage (112h)

A47

Create relational tables for a defined data model.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP16

Linked subjects

Data Warehouses

A48

Construct SQL queries for requested data retrieval.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP16

Linked subjects

Data Warehouses

A49

Design star schemas for analytical data storage.

1 competences | 1 subjects

create · 5/6

See details

Linked competences

CP17

Linked subjects

Data Warehouses

A50

Map source data into warehouse staging tables.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP17

Linked subjects

Data Warehouses

A51

Validate warehouse loads against source records.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP17

Linked subjects

Data Warehouses

A52

Optimize OLAP queries for analytical performance.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP17

Linked subjects

Data Warehouses

A53

Define sampling criteria for a data collection task.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT2

Linked subjects

Data Mining

A54

Design search queries for retrieving structured data.

1 competences | 1 subjects

create · 5/6

See details

Linked competences

CT2

Linked subjects

Data Mining

A55

Evaluate data completeness against business reporting requirements.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CT3

Linked subjects

Data Mining

A56

Classify data inconsistencies against quality rules.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CT3

Linked subjects

Data Mining

A57

Assess data accuracy against source records.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CT3

Linked subjects

Data Mining

A58

Validate dataset quality before loading it into analysis workflows.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CT4

Linked subjects

Data Mining Data Analysis and Programming In R

A59

Structure quantitative data into analysis-ready tables.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT4

Linked subjects

Data Analysis and Programming In R

A60

Interpret data patterns to support reporting decisions.

1 competences | 1 subjects

analyze · 4/6

See details

Linked competences

CT4

Linked subjects

Data Analysis and Programming In R

A61

Present quantitative findings in clear visual summaries.

1 competences | 1 subjects

create · 5/6

See details

Linked competences

CT4

Linked subjects

Data Analysis and Programming In R

A62

Present analysis results to a technical audience.

1 competences | 0 subjects

responding · 2/5

See details

Linked competences

CT6

Linked subjects

(no linked subjects)

A63

Interpret research results against project hypotheses.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CT6

Linked subjects

(no linked subjects)

A64

Classify copyright status for software deliverables.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CT7

Linked subjects

(no linked subjects)

A65

Document ownership claims for project artifacts.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CT7

Linked subjects

Msc Thesis Preparation

Responsibility

19 entries

Evaluate program output against expected input conditions.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP3

Linked subjects

Programming I

Evaluate data quality against processing requirements.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP4

Linked subjects

(no linked subjects)

Verify schema consistency after normalization changes.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP10

Linked subjects

Databases

Verify dataset integrity after cleansing actions.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP11

Linked subjects

(no linked subjects)

Evaluate retained information after reduction.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CP13

Linked subjects

(no linked subjects)

Compare current customer needs against available evidence.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP18

Linked subjects

Multi-agent Systems Msc Thesis Preparation

Validate calculation outputs against problem constraints.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP19

Linked subjects

Operations Research and Optimization

Assess machine learning outputs against validation data.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP20

Linked subjects

Dynamical Systems In Machine Learning Machine Learning

Apply open data principles to research dataset publication.

1 competences | 1 subjects

apply · 3/6

See details

Linked competences

CP21

Linked subjects

Big Data Technologies

R10

Store datasets using approved organisational procedures.

1 competences | 0 subjects

apply · 3/6

See details

Linked competences

CT5

Linked subjects

(no linked subjects)

R11

Reconcile conflicting values in integrated datasets according to source rules.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP7

Linked subjects

Data Warehouses Special Topics In Artificial Intelligence

R12

Audit identity matches for duplicate records.

1 competences | 1 subjects

evaluate · 6/6

See details

Linked competences

CP8

Linked subjects

Data Warehouses

R13

Monitor data usage controls against organisational access policies.

1 competences | 2 subjects

analyze · 4/6

See details

Linked competences

CP9

Linked subjects

Research Ethics Professional Practice

R14

Track submitted records for completeness before approval.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CP14

Linked subjects

Professional Practice Practice Stage (112h)

R15

Schedule routine backups for digital datasets.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP15

Linked subjects

Professional Practice Practice Stage (112h)

R16

Update database records within transaction boundaries.

1 competences | 2 subjects

apply · 3/6

See details

Linked competences

CP16

Linked subjects

Data Warehouses Professional Practice

R17

Evaluate retrieved data against collection requirements.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CT2

Linked subjects

Big Data Technologies Big Data Applications

R18

Judge data usability for a defined business purpose.

1 competences | 2 subjects

evaluate · 6/6

See details

Linked competences

CT3

Linked subjects

Data Science Industry Project Predictive Models and Analytics

R19

Evaluate license obligations for third-party code.

1 competences | 0 subjects

evaluate · 6/6

See details

Linked competences

CT7

Linked subjects

(no linked subjects)

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

Probabilistic Models For Data Science

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

C12 C13 A23

Data Analysis and Programming In R

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

A58 A59 A60 A61

Operations Research and Optimization

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C11 R7

Research Ethics

2 credits

Lecture 1 Seminar 0 Lab 0 Practical 0 Kind DC

C21 R13

Elective subjects

5 credits

Distributed Systems

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C18 A30 A31 A32

Advanced Logic and Functional Programming

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C2 A5

5 credits

Distributed Methods and Technologies Based On Xml

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C19 A33 A34 A37 A38

Dynamical Systems In Machine Learning

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C12 R8

Optional subjects

Volunteering I (60h)

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)

Databases

5 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DS

C5 C6 A1 A2 A3 A11 A12 A13 A14 A15 R3

Programming I

6 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DF

C1 C2 A5 A16 A17 R1

Programming III

5 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DS

(no linked learning outcomes)

Semester 2

Credits: 30 | Total hours/week: 24

Mandatory subjects

Data Warehouses

5 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C19 C24 A33 A34 A37 A38 A43 A44 A47 A48 A49 A50 A51 A52 R11 R12 R16

Data Mining

5 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C15 C20 C21 C22 C25 A26 A27 A35 A36 A38 A39 A40 A41 A53 A54 A55 A56 A57 A58

Big Data Technologies

5 credits

Lecture 1 Seminar 0 Lab 2 Practical 0 Kind DS

C14 A24 A25 R9 R17

Practice Stage (112h)

5 credits

Lecture 0 Seminar 0 Lab 1 Practical 8 Kind DS

A45 A46 R14 R15

Elective subjects

5 credits

Parallel Computing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C10

Special Topics In Artificial Intelligence

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

A33 R11

5 credits

Multi-agent Systems

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C9 A20 R6

Biostatistics and Medical Data Analysis

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C4 C12 C13 A10 A23

Optional subjects

Volunteering II (60h)

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)

Programming II

6 credits

Lecture 2 Seminar 0 Lab 3 Practical 0 Kind DS

(no linked learning outcomes)

Semester 3

Credits: 30 | Total hours/week: 15

Mandatory subjects

Machine Learning

6 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DS

C12 R8

Big Data Applications

6 credits

Lecture 1 Seminar 0 Lab 2 Practical 0 Kind DS

A42 R17

Data Science Industry Project

6 credits

Lecture 0 Seminar 0 Lab 2 Practical 0 Kind DS

R18

Elective subjects

6 credits

Predictive Models and Analytics

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

R18

Text Mining

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C8 A39 A40

Introduction To Quantum Computing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C10 C11

6 credits

Metaheuristic Algorithms

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C11

Statistical Methods For Clinical Studies

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C12 A8 A10

Computer Vision

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

C8 A18 A19

Optional subjects

Volunteering III (60h)

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 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

C21 C23 A42 A43 A44 A45 A46 R13 R14 R15 R16

Msc Thesis Preparation

15 credits

Lecture 0 Seminar 0 Lab 5 Practical 0 Kind DS

C9 A20 A21 A65 R6

Scientific Seminar

7 credits

Lecture 0 Seminar 0 Lab 2 Practical 0 Kind DS

C9 A21

Elective subjects

(no elective groups)

Optional subjects

Volunteering IV (60h)

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)