Tentative course schedule

Week 1: Introduction to Data visualization Web development 

Week 2: Javascript 

Week 3: More Javascript 

Week 4: Data Data driven documents (D3.js)

Week 5: Interaction, filtering, aggregation (UI /UX). Advanced D3 / javascript libs

Week 6: Perception, cognition, color Marks and channels

Week 7: Designing visualizations (UI/UX) Project introduction Dos and don’ts for data-viz 

Week 8: Maps (theory) Maps (practice)

Week 9: Text visualization

Week 10: Graphs

Week 11: Tabular data viz Music viz

Week 12: Introduction to scientific visualisation

Week 13: Storytelling with data / data journalism Creative coding

Week 14: Wrap-Up 

Data viz, visualization, data science

CS-305 Software engineering (BA)

CS-250 Algorithms (BA)

CS-401 Applied data analysis (MA)

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

CS-486 Interaction design (MA)

CS-210 Functional programming (BA)

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.

• 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

• 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.

Ex cathedra lectures, exercises, and group projects

• 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.

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

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

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

The Truthful Art: Data, Charts, and Maps for Communication by Cairo, Alberto. Royaume-Uni, New Riders, (2016).

Data Visualisation: A Handbook for Data Driven Design by Kirk, Andy. Royaume-Uni, SAGE Publications, (2019).

• Data Visualisation / Kirkhttp://library.epfl.ch/en/beast?isbn=9781526468932
• Visualization Analysis and Design / Munznerhttp://library.epfl.ch/en/beast?isbn=9781466508910
• Interactive Data Visualization for the Web / Murrayhttps://slsp-epfl.primo.exlibrisgroup.com/permalink/41SLSP_EPF/6bhkks/cdi_safari_books_9781491921296
• The Truthful Art / Cairohttp://library.epfl.ch/en/beast?isbn= 9780321934079

Lecture notes

• https://go.epfl.ch/COM-480https://go.epfl.ch/COM-480