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.
Dataset types — tables, charts, networks, fields, geometry, hiereachies. Data scales — nominal, ordinal, interval, ratio. Data ordering — sequential, diverging, cyclic.
Low code/no-code perspectives on getting data. Scraping sites using ParseHub.
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.
Low code/no-code tools to manipulate data using tools like Pivot Tables. Editor tooling and data merge using javascript
A primer for HTML, CSS and Javascript.
4 levels of visualization validation: domain/problem → data/task abstraction → viz idiom → algorithm / technology. Case studies and examples.
Using Git for projects - coordination, rollbacks, back-ups. Also using GitPages for deployment.
Connect to NEA's 2-hr SG weather forecast API and print out the results in a nicely formatted HTML table.
Defining how visualizations are composed of marks and channels. Analyzing the effectiveness of visual channels through accuracy, discriminability, salience, separability.
Yi Zhuan is the product manager of data.gov.sg, where he's trying to make Singapore's open data more discoverable, understandable and usable.
Learn more about the design and product decisions that went into building a data sharing platform, as well as how you can help to build the open data ecosystem in Singapore.
Critique of 2 visualizations. Critique will be presented to the class for discussion.
Workshop on visualization design and validation. Introduction to how the web draws graphics, and the SVG specification.
President's Welcome Dinner for MTD postgraduate students.
Color: perception, specification and use. How the eye sees color. Colorspaces. Implications for color use as a visualization channel.
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 HDB's transaction data (what), and then design and draw one or more charts (how).
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.
D3's geo projection library. GeoJSON and TopoJSON formats. Choropleth viz (D3).
A simple Singapore choropleth viz mashing up population with sub-district data.
Presentations by students on assignment 2 & 3.
Presentations by students on assignment 2 & 3.
This talk explores how LLMs convert natural language prompts directly into code for popular JavaScript and Python libraries, helping both domain experts and developers visualize data quickly.
A deeper dive into various different geospatial visualizations. Choropleth maps, isochrone / contour maps, etc.
We'll take a look at a report on the state of the data visualization industry from the Data Visualization Society (DVS) survey 2023.
Zen and the Art of Data Storytelling. The title says it all, as Yong Jun will be going through past data visualization projects that hope to impress, inspire, and ignite curious minds.
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.
Final presentations.