Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:
- 12-15 ECTS in technical scientific modules (TSM)
TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
- 9-12 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
- 6-9 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.
In the module description (download pdf) you find the entire language information per module divided into the following categories:
The module is organised around 4 core subject areas:
- Data Preprocessing
- Data Classification
- Complex Networks
- basic python scripting
- basic mathematical analysis and linear algebra
Students understand how to use database technologies and data analysis tools and languages to process large data collections.
- They learn the basics of the analysis of large data sets
- They know the main tools to address analysis of large data sets
- They will learn and use the most common classification techniques
- They will learn methods for processing and clustering with the purpose of effective analysis
- They can reuse the material acquired in this course in their own working environment and apply them to solve their specific problems
- They know the current research directions within these domains.
Contents of Module
The content of the module is the following:
- Introduction to data analysis
- Data Preprocessing (noise and outliers, aggregation, PCA, features selection, etc. )
- Linear Regression, Logistic Regression
- Data Classification and classifier evaluation
- Clustering and cluster validation
- Recommendation Systems
- Complex Networks
Teaching and Learning Methods
Problem based learning. During the lesson the lecturer will introduce real world problems and the class will try to solve them together. The lecturer will support the problem solving process, introducing new concepts, as required.
Lecture slides, references to internet resources and books