COM-480 / 4 crédits

Enseignant: Vuillon Laurent Gilles Marie

Langue: Anglais


Summary

Understanding why and how to present complex data interactively in an effective manner has become a crucial skill for any data scientist. In this course, you will learn how to design, judge, build and present your own interactive data visualizations.

Content

Keywords

Data viz, visualization, data science

Learning Prerequisites

Required courses

CS-305 Software engineering (BA)

CS-250 Algorithms (BA)

CS-401 Applied data analysis (MA)

Recommended courses

EE-558 A Network Tour of Data Science (MA)

CS-486 Human computer interaction (MA)

CS-210 Functional programming (BA)

Important concepts to start the course

Being autonomous is a prerequisite, we don't offer office hours and we won't have enough teaching assistants (you've been warned!).

Knowledge of one of the following progrmaming language such as C++, Python, Scala.

Familiarity with web-development (you already have a blog, host a webiste). Experience with HTML5, Javascript is a strong plus for the course.

 

Learning Outcomes

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

  • Judge visualization in a critical manner and suggest improvements.
  • Design and implement visualizations from the idea to the final product according to human perception and cognition
  • Know the common data-viz techniques for each data domain (multivariate data, networks, texts, cartography, etc) with their technical limitations
  • Create interactive visualizations int he browser using HTM5 and Javascript

Transversal skills

  • Communicate effectively, being understood, including across different languages and cultures.
  • Negotiate effectively within the group.
  • Resolve conflicts in ways that are productive for the task and the people concerned.

Teaching methods

Ex cathedra lectures, exercises, and group projects

Expected student activities

  • Follow lectures
  • Read lectures notes and textbooks
  • Create an advanced data-viz in groups of 3.
  • Answer questions assessing the evolution of the project.
  • Create a 2min screencast presentation of the viz.
  • Create a process book for the final data viz.

Assessment methods

  • Data-viz (35%)
  • Technical implementation (15%)
  • Website, presentation, screencast (25%)
  • Process book (25%)

Supervision

Office hours No
Assistants No
Forum No

Resources

Bibliography

Visualization Analysis and Design by Tamara Munzner, CRC Press (2014). Fee online version at EPFL.

Interactive Data Visualization for the Web by Scott Murray O'Reilly (2013) - D3 - Free online version.

Ressources en bibliothèque

Notes/Handbook

Lecture notes

Websites

Moodle Link

Dans les plans d'études

  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Data visualization
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
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20-21     
21-22