Ce cours apparaît dans les formations suivantes :
• IÉSEG > MSc in Big Data Analytics for Business > Semester 2 > 4,00 ECTS

Pré requis

This is a mathematically and computationally oriented course, where students are expected to have previously completed basic courses in Differential and Integral Calculus, Linear Algebra, and Computer Programming.

Objectifs du cours

At the end of the course, the student should be able to:
- define and model network structures
- manipulate network data using specialized softwares
- use different modeling strategies and algorithmic methods for network analysis
- build and analyze a company's social network using available company data

These competencies and/or skills contribute to the following learning objectives
- 1.A Demonstrate an international mindset
- 2.A Assess the values of the organization in which they work
- 2.B Solve professional dilemmas using concepts of CSR and ethics
- 5.C Employ state-of-the-art management techniques
- 5.D. Make effectual organizational decisions
- 7.C Effectively apply in-depth specialized knowledge to take advantage of contemporary opportunities in their professional field

Contenu du cours

This is a graduate course in Social Network Analysis, which is designed to enable students to correctly manipulate network data, and to design statistical models for this class of data. The first part of the course focuses on Graph Theory, Network Data Structures, and Network Data Manipulation. The second part of the course deals with the classical network analysis toolbox: Centrality, Transitivity, Community Detection and Blockmodeling. The third part of the course focuses on Random Graphs and provides students with a collection of computational tools to correctly solve the designed models. The course is based on the use of several computational methods, which are currently implemented in R packages.

Modalités d'enseignement

Organisation du cours

Méthodes pédagogiques

• Interactive class
• Presentation
• Project work

Évaluation

The students evaluation is based on an individual assignment and a group project.

Bibliographie

• U. Brandes and T. Erlebach, NETWORK ANALYSIS: METHODOLOGICAL FUNDATION -