There will be no class during CNY celebrations. Make-up to be done during recess or end of the semester.
Introduction. Course schedule and expectations. Why do data visualization? Historical examples. Exploratory vs. explanatory. A visualization analysis framework.
Big picture questions. Why are computers and humans involved in data visualization? Why show the data? Why allow people to interact with it? What resource limitations are there? Also, course admin.
Lecturer on compassionate leave. No class for a week.
Dataset types — tables, charts, networks, fields, geometry, hiereachies. Data scales — nominal, ordinal, interval, ratio. Data ordering — sequential, diverging, cyclic.
Why are people using data viz? We'll analyze by looking at a high-level framework of actions and targets. Actions: Analyze, search, query, etc. Targets: Trends , outliers , features, etc.
A refresher on web core technologies – HTML / CSS / JS. Useful JS design patterns that you will see over and over again. Function chaining, interfacing with backend systems with JSON and AJAX, dealing with asynchronous issues with callbacks and promises.
Pull out data.gov.sg's PSI API data and populate it into a simple HTML table. Prep for future assignments / the project.
4 levels of visualization validation: domain/problem → data/task abstraction → viz idiom → algorithm / technology. Case studies and examples.
Defining how visualizations are composed of marks and channels. Analyzing the effectiveness of visual channels through accuracy, discriminability, salience, separability.
Find 2 bad visualizations and critique them using what we've learnt. Also suggest ways to correct them.
Workshop on visualization design and validation. Introduction to how the web draws graphics, and the SVG specification.
Introduction to D3 (Data Driven Documents), the defacto data visualization library for the web. Toolchains and other libraries. Selections, domains, ranges, axes. Enter, update, exit - design pattern for glassy transitions.
Frame a hypothesis (why), query one of data.gov.sg's datasets (what), and then design and draw one or more charts (how).
Week 7 half-term academic break
Presentations by students on assignment 2: Two bad visualizations, why and ways to make them better.
Color: perception, specification and use. How the eye sees color. Colorspaces. Implications for color use as a visualization channel.
Presentations by students on assignment 3.
We will also do a class-based discussion on some visualization case studies. In an era of AI/LLMs, knowing how to frame why / what / how we're approaching visualizations is more important than execution.
A deeper dive into various different visualizations that are used for tabular and hierarchical data. Bar, line, scatterplot, streams, etc. Packed circles, treemaps, dendrograms, etc. Use case examples.
Hierarchical / network chart types. Force directed networks, treemaps, sankeys and arc diagrams. Use case examples.
The OGP (Open Government Products) team will be sharing how they build a data visualisation product, from ideation, design, iteration to production.
Learn from all the mistakes they made while trying to visualise Singapore's Elections data!
Check out OGP's elections data visualization at elections.data.gov.sg
A deeper dive into various different geospatial visualizations. Choropleth maps, isochrone / contour maps, etc.
D3's geo projection library. GeoJSON and TopoJSON formats. Choropleth viz (D3).
A simple Singapore choropleth viz mashing up population with sub-district data.
Leaflet, 2.5D and 3D geospatial viz. Building a simple viz layer on top of a raster base map.
Techniques to consider for improving visualizations. Manipulating views - changes, selection and navigation. Faceting and layers. Focus + context (embedding).
Guidelines on 3D use, animation and interaction. Latency and feedback. A summary and future perspectives.
Presentations by students on Assignment 5, which is on the project itself. What is the dataset, why are they doing the visualization (bottom-up or top-down) and how do they intend to put it together?
We'll take a look at some case studies and go through them in a class discussion setting.
We'll take a look at a report on the state of the data visualization industry from the Data Visualization Society (DVS) survey 2024.
Final presentations.