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.

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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, updated to 10. April, updated to 14. April , 4pm UK time

Lectures

Lectures are grouped into four parts:

Overview

Week 1: Course Introduction

Course and assignmeent overview. Introduction into basics of data visualization

Lecture Slides:

Just discovering after the lecture that on Jan 7, the BBC wrote an article about the misleading visualization about Australian bushfires, discussing alternative visualizations. Quote: “But it is actually artist Anthony Hearsey’s visualisation of one month of data of locations where fire was detected, collected by Nasa’s Fire Information for Resource Management System. […] The scale is a little exaggerated due to the render’s glow, but it is generally true to the info from the Nasa website. Also note that not all the areas are still burning, and this is a compilation,” Mr Hearsey wrote on Instagram in response to criticism by viewers that the image was misleading.”

Week 2: Foundations I: Basic Concepts

This lecture introduces the basics concepts of data visualizaion: Why are we using data visualization? Why does visualization work?. The lecture includes topics such as perception and color and concepts such as exploratory data analysis, data-driven storytelling, visual variables, visual mappings, visualization pipeline, visualization techniques, visual representation, and visualization literacy.

Required Reading:

Lecture Slides:

Further Reading

Week 3: Visualization Design and Color

Foundations: Visualization design

This lecture talks about effective and ineffective visualization designs. It shows examples of ineffective and deceptive visualizations and describes visualization guidelines that help designing effective visualiztions. The corresponding tutorial will exercise over common problems with visualizations and work towards proposing solutions via sketching, a powerful method introduced in the tutorial. r Lecture Slides:

Required Reading:

Further Reading

Video Lecture—Foundations: Color for Data Visualization

This lecture provides a brief introduction and overview over color perception and the use of color scales in data visualization. It also covers color blindness, color across cultures and some simple tools to use color.

Further reading

Week 4: Tools & Basic Charts

Foundations: Tools for data visualizations

This lecture overviews and introduces common software applications (tools) to help with both: data analysis and the creation of visualizations. We will overview tools for programming environments such as python (e.g., Seaborn) and javascript (e.g., D3), but also tools using common user interfaces (e.g, Rawgraph, Datawrapper). The lecture will not teach how to use these tools, but focus on a high-level overview of the many different tools and workflows exist to create data visualizations.

Lecture Slides

Links

Video Lecture—Techniques: Basic Charts

This is the first of a set of lectures on visualization techniques, i.e., charts and visual representations. This lecture gives a brief overview over basic charts often used in scientific reports and news papers. This includes histograms, barcharts, violinplots, boxplot, linecharts, and piecharts.

Further reading

Week 5. Techniques: Multidimensional data and Trees & Hierarchies

Techniques: Multidimensional data

As multidimensional data we can describe all sorts of data sets that have a variety of attributes, for example columns in table. Generally speaken, the more columns a dataset has, the more dimensions it has. This lecture talks about visualization techniques for multivariate data such as scatterplots, glyphs, Mekko Chart, Heatmaps, Beeswarm plots, Scatterplot matrices, Parallel Coordinates Plots, and Multi-dimensional scaling.

Lecture Slides

Further Reading

Video Lecture— Techniques: Trees and Hierarchies

Lecture Slides

Recordings

Further Reading

Week 6. Techniques: Networks and Geographic Data

Techniques: Networks

Required Reading

Lecture Slides

Video Lecture—Techniques: Geographic Data

Lecture Slides

Videos

Further Reading

Week 7. Techniques: Temporal Data

Mandatory Reading Glance at this paper. No need to read everything but try to get an idea of the space-time cube metaphor that the paper is talking about and how it’s used to describe data visualization.

Techniques: Temporal Data

Lecture Slides:

Further reading

Week 8. Advanced: Storytelling and Communication

This lecture focuses on effective presentation techniques when using visualizations in e.g., infographics or presentations. Other presentation media can include videos, posters, and datacomics. The lecture investigates how presenting and talking with visualizations is different than using visualizations for exploring and analyzing data. Spoiler alert: “It’s the audience, stupid!”.

Lecture Slides:

Further Reading:

Week 9. Guest Lecture: Renaud Blanch

cancelled due to University shutdown

Week 10. Advanced: Interction for Visualization

This lecture talks about the need for interaction in visualization and will present a range of interaction techniques for specific visualization related tasks: select, explore, reconfigure, encode, abstract, filter, connect.

Lecture Slides:

Lecture recording

Further Reading:

Week 11. Advanced: Evaluating visualization techniques

This lecture covers techniques to assess if a given visualization technique (existing or your own creation) is “successful”. Successful is a broad term and refers to both effectiveness and efficiency in which a user or audience are supported in their analysis or understanding of the data. The lecture proposes a simple heuristic, Readability, Understandability, Supportiveness_, Truthfulness, Insightfulness and Communication.

Lecture Slides:

Lecture recording

Further Reading: