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.

Goals

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)

Assessment method

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

  • Technical report about the techniques used in the project 
  • Vulgarized presentation about the output of the project 
  • Poster presentation to represent the relevance of the project visually 

Sources, references and any support material

Slides are available through Webcampus

Language of instruction

Français