Coursebooks 2017-2018

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Network analytics

MGT-416

Lecturer(s) :

Penner Orion B

Language:

English

Summary

Students will learn the core concepts and techniques of network analysis. 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 - drawn from social network analysis, graph theory, and network science - for analyzing real world network data. Throughout the course theoretical material will be presented in parallel with data and code. Assignments and the final project will require students to handle, analyze and interpret real network data using R or Python in the Jupyter Notebook environment.

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

Care will be taken to reinforce these techniques and concepts with examples using real world data, and to faciliate the development of intuition for when and how networks are a useful avenue of analysis.

Keywords

Data Analysis; Business Analytics; Statistics; Complex Systems; python; R

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

It is strongly recommended that each student complete an introductory course in either python or R before the start of the course. Many MOOCs and/or tutorials are available online.

Statistics and probability experience beyond the introductory level are also recommended.

Learning Outcomes

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

Transversal skills

Teaching methods

Weekly lectures integrating both theory and application. Computational examples will be explored using Jupyter Notebooks. Accompanying exercise sessions will give students "hands on" experience writing and running analysis code, and interpreting results.

Expected student activities

*Notes and assignments containing R and Python code will be made available to students through Jupyter Notebooks by way of Docker containers. Hence, it is not necessary that students install R and/or Python themselves, although students will find benefits to having their own installation of one or the other for the final project.

Assessment methods

Regular assignments: 50%

Individual end-of-year project: 40%

Class and exercise session participation: 10%

Supervision

Office hours Yes
Assistants Yes
Forum No

Resources

Virtual desktop infrastructure (VDI)

Yes

Bibliography

Lecture notes and Jupyter Notebooks will represent the bulk of course material, but the following references may prove useful for various topics*:

*Sections of interest will be specifically noted in course notes. Small portions may be distributed as necessary, and as may be consistent with intellectual property constraints.

Ressources en bibliothèque
Websites

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17 CHB330   
17-18    
18-19 CHB330   
19-20     
20-21     
21-22     
 
      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