DataVis 2020

Course website Data Visualization 2020 course at University of Edinburgh. Check here for updates and course materials. Learn will be used for assignments only.

Navigation

Overview
Lectures
Tutorials
Assignments
Vis Guidelines

Course organizer: Dr. Benjamin Bach
Lecture:Mondays 10-11, 7 George Sq, S.1

Deadlines

Assignment 1: 26. February 2020, 4pm UK time
Assignment 2: 3. April 2020, 4pm UK time

Data Visualisation 2019/20

This is the only website where to find learning material about the course. The website will be updated regularly thoughout the course with the latest slides and tutorial information. Learn will be used for assignments only! Bookmark this page.

Course content and learning outcomes

The course aims to provide a general understanding of how to use data visualizations in your work and how to be critical about it. The course has three specific learning outcomes which you assignments will be marked against.

  1. Analysis: The ability to identify and describe a visualization challenge in terms of context, stakeholders, data, and tasks.
  2. Design: Design and implement a visualization through one of various media (interactive, infographic, data comic) and through a self-chosen set of tools. Visualization designs are meant to match an earlier identified challenge.
  3. Evaluation: Critically reflect on a visualization design and suggest constructive solutions to identified challenges. ’

The course teaches:

Course assignments will require you to find a data set you would like to work with during the course. Students are free to chose the data set they would like to work with, but the data must be related to climate change or to any of the UN millennium goals. If you are not sure about the data you chose, talk to me after the lecture. Student work around data on climate change can enter a competition for a possible public exhibition/conference in November, associated to the COP26 in Glasgow. Data can come from any source as long as privacy and copy rights are respected: the internet, an external collaborator, the student’s own research, another course the student is taking, personally collected data.

Course Organization

This course has 11 lectures, 5 tutorials and 2 assignments. Any lecture is 2h, including 90min lecturing, a 10min break, and 20min for question and answering. Each course week comes with a small home work which should not take more than XX h.

There are 2 assignments, both need to be handed in. There is no written exam. Students will work individually for assignment Assignment 1 and in groups of 3 for Assignment 2.

A detailed description and slides for each lecture is found here

Date Session Lecture Tutorial Assignments
Jan, 13 1 Foundations I: Introduction to Data Vis
Jan, 20 2 Foundations II: Visualization design T1: Critique+Redesign
Jan, 27 3 Foundations III: Tools for data visualizations
Feb, 03 4 Techniques I: Visualizing Statistical and Multivariate Data T2: Challenge+Design
Feb, 10 5 Techniques II: Trees, Networks, and Sets Assignment 1
Feb, 17 - Week of creative learning
Feb, 24 6 Techniques III: Geographic and Temporal T3: Storytelling
Mar, 02 7 Advanced I: Storytelling and Communication  
Mar, 09 8 Advanced II: Evaluating visualization techniques T4: Atelier
Mar, 16 9 guest lecture
Mar, 23 10 Topic Lecture T5: Atelier
Mar, 30 11 Final Presentations Assignment 2

Additional Course material (optional)