This is an online course on data visualization for upskilling professionals in all sectors running 04. May – 10. July 2021


Course Description
Teaching Approach
Topics & Schedule

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Topics & Schedule

The schedule is a guide and subject to change. Optional courses and hand-on tutorials especially may be adapted to meet more closely the needs of participants.

The course is expected to require a total time investment of 5-10h / week, roughly split into

The course features

Schedule Overview

Each week will focus on one key topic:

Detailed Schedule

Week 1: Introduction and Foundations

Introduction into basics of data visualization. Why are we using data visualization? Why does visualization work? What are forms of visualization? What is the difference between exploration and explanation. What is of visualization literacy? The week’s second session introduces the visualizaton design process, basics of design thinking, and understand the idea behind Exploratory Data Analysis (EDA).

The week will see a sketching exercise, and tutorial on basic charts and common flaws in visualizations. You will formulate a visualization challenge for your class project.

Week 2: Visualization Design

Basic concepts of visualization design: data types, visual variables, color, design guidelines.

You will engage in sketching and ideation for your class project.

Week 3: Visualization Tools

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.

The tutorial will discuss visualization tools with us and the peers. We also run a sneak-preview tutorial on D3.

Week 4: Visualization Techniques I

Choose between a selection of lectures and readings on

You will work on preparing and exploring your data for your class project.

Week 5: Visualization Techniques II

Choose between a selection of lectures and readings on

You will engage in critical analysis of your class project progress.

Week 6: Storytelling

This lecture focuses on effective presentation and communication techniques when using visualizations in e.g., infographics and presentations. Other presentation media can include videos, posters, or data comics. The lecture investigates how presenting and talking with visualizations is different than using visualizations for exploring and analyzing data.

In the tutorial, you will have the chance to work on a storyboard for the story you want to tell with your data.

Week 7: Data Physicalization & Personal Data Visualization

Physical visualization and visualization of personal data.

This week gives time to work on your project.

Week 8: Project 1-on-1s

This week is featuring no content but gives you time to work on your project. You will have the chance to meet the organizers in a private session to discuss your projects.

Week 9: Evaluation

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

Week 10: Interaction

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.

You will continue work on your project and run a mini-evaluation with your peers.

Guest Talks

We will host a variety of guest talks, some of which will be run in conjunction with the Edinburgh Data Visualisation Meetup. Talks last year covered a variety of areas, with speakers’ backgrounds across diverse areas including: