Coursebooks

Network analytics

MGT-416

Lecturer(s) :

Kiyavash Negar

Language:

English

Summary

Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and application will be balanced, with students working directly with network data throughout the course.

Content

This course will cover a broad range of approaches pertaining to network causal analysis for analyzing real world network data ranging from financial to social and biological networks. The assignments, mid-term and final project will require students to have a theoretical understanding of the concepts as well as to be able to analyze and interpret real network data.

Specific topics include, but are not limited to, the following:

Keywords

Statistics; Causal Inference; Network Analytics

Learning Prerequisites

Required courses

This course attempts to be as self contained as possible, but it does approach the topic from a quantitative point of view and, as such, students should be comfortable with the basics of (i.e. have taken at least one course in) the following topics before enrolling:

As course work will be largely computational, experience with at least one programming language is also required.

Recommended courses

Statistics and probability experience beyond the introductory level are recommended.

Learning Outcomes

By the end of the course, the student must be able to:

Transversal skills

Teaching methods

The weekly lectures integrate both theory and application. Exercise sessions give students "hands on" experience writing and running analysis code, and interpreting results. In both, care is taken in both to help develop computational thinking skills.

Expected student activities

 

Assessment methods

Regular individual assignments: 45%

Final group project: 55%

Supervision

Office hours Yes
Assistants Yes
Forum No

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Lecture notes.

Ressources en bibliothèque

In the programs

Reference week

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Under construction
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German