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 and SQL
- basic calculus, linear algebra and statistics concepts
Students understand how to use data analysis tools to process large, structured and heterogeneous data collections.
- They learn the basics of the data analysis
- They know the main tools and techniques to address the analysis of large data sets
- They learn and use the most common classification techniques
- They learn how to exploit the networking structure of the data to handle the complexity and dynamicity of large set of data
- They learn the main tools for data and results visualization
- They 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 (univariate and bivariate analysis, features selection, dimensionality reduction)
- Linear Regression, Logistic Regression
- Data Classification, Bagging and Boosting, classifiers evaluation
- Clustering and clustering validation
- Recommendation Systems
- Complex Networks Theory
- Network measures and Models
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 and tools, as required.
Practical work will complement the theory, so that students can put in practice the studied arguments.
Lecture slides, references to internet resources and books