MSE Master of Science in Engineering

The Swiss engineering master's degree


Jedes Modul umfasst 3 ECTS. Sie wählen insgesamt 10 Module/30 ECTS in den folgenden Modulkategorien:

  • ​​​​12-15 ECTS in Technisch-wissenschaftlichen Modulen (TSM)
    TSM-Module vermitteln Ihnen profilspezifische Fachkompetenz und ergänzen die dezentralen Vertiefungsmodule.
  • 9-12 ECTS in Erweiterten theoretischen Grundlagen (FTP)
    FTP-Module behandeln theoretische Grundlagen wie die höhere Mathematik, Physik, Informationstheorie, Chemie usw. Sie erweitern Ihre abstrakte, wissenschaftliche Tiefe und tragen dazu bei, den für die Innovation wichtigen Bogen zwischen Abstraktion und Anwendung spannen zu können.
  • 6-9 ECTS in Kontextmodulen (CM)
    CM-Module vermitteln Ihnen Zusatzkompetenzen aus Bereichen wie Technologiemanagement, Betriebswirtschaft, Kommunikation, Projektmanagement, Patentrecht, Vertragsrecht usw.

In der Modulbeschreibung (siehe: Herunterladen der vollständigen Modulbeschreibung) finden Sie die kompletten Sprachangaben je Modul, unterteilt in die folgenden Kategorien:

  • Unterricht
  • Dokumentation
  • Prüfung
Data Analysis and Classification (TSM_DataAnaCla)

The module is organised around 4 core subject areas:

  • Data Preprocessing
  • Data Classification
  • Clustering
  • Complex Networks

Eintrittskompetenzen

 

  • basic python scripting and SQL
  • basic calculus, linear algebra and statistics concepts

 

Lernziele

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.

Modulkategorie

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

Lehr- und Lernmethoden

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.

Bibliografie

Lecture slides, references to internet resources and books

Vollständige Modulbeschreibung herunterladen

Zurück