Course 2024-2025

Data Analytics Project [IDASM104]

  • 5 credits
  • 30h+15h
  • 1st quarter
Language of instruction: French / Français

Learning outcomes

At the end of this course, students will be capable to carry out a data analytics project, using the skills acquired in “Machine Learning and Data Mining” (IDASM102), “Information Visualization” (IDASM103) and “Graph Mining” (SDASM101).  They will be able to analyze data and present the results of their analysis in an efficient and understandable way.

Objectives

The objective of this course is to get the student to:

  • Act as a Data Scientist to exploit Open Data and create, develop, and communicate an innovative project
  • Apply Information Visualization, Machine Learning and Graph Mining techniques and tools to a concrete use case
  • Work as an interdisciplinary team on a concrete data analytics project

Content

  • Introduction Session
    • Guidelines of the project
    • Open Data Presentation
    • External Intervention by Nicolas Installé (Head of Futurocité)
  • Brainstorming Session
    • Brainstorming Principles
    • User-Centered Data Analytics
    • Pitching Principles
  • Intermediary Pitch
    • Feedback to the students about the idea aspects, the technical aspects, and the implementation aspects
    • Poster design guidelines
  • Technical Coaching Session
    • Free session where groups can ask questions to the professors individually
  • Final Presentation

 

Table of contents

  • Technical report about the techniques used in the project (/7,5)
  • Vulgarized presentation about the output of the project (/7,5)
  • Poster presentation to represent the relevance of the project visually (/5)

Co-requisites

Visualisation de l'information [IDASM103] et Machine learning et data mining [IDASM102]

Teaching methods

“Hackathon”-style course where students are asked to work independently on an applied data analytics project. The classroom sessions facilitate the work of the students. Students work on the project in a “learning by doing” approach. 

Evaluations

The knowledge gained from the course is evaluated in three ways:

  • Technical report about the techniques used in the project (/7,5)
  • Vulgarized presentation about the output of the project (/7,5)
  • Poster presentation to represent the relevance of the project visually (/5)

Recommended readings

Slides are available through Webcampus

Language of instruction

French / Français

Location for course

NAMUR

Organizer

Faculté d'informatique
rue Grandgagnage 21
5000 NAMUR
P. 081725252
F. 081724967
secretariat.info@unamur.be

Degree of Reference

Master's Degree