It is amazing to me personally that this talk was almost entirely about introducing one
of the most underrated coding language in data viz, D3.js, which is a lang that should
be a staple in every team that wants to create bespoke charts and design beautiful yet
functional dashboards from scratch and proudly enough my master’s thesis main topic
is about a SaaS that has a dashboard that will be created entirely by the unpopular D3.js.
I was and will be involved in designing all the components needed for the dashboard and also in the appropriate research to find out how to develop those components on a web app level which is was done later by a fellow full stack developer.
What is the talk about
- The talk shows how to build a unique, effective — and “beautiful” — data visualization from nothing but a blank browser window, using D3.js.
- The goal isn’t simply to produce a standard chart, but to think creatively and intentionally — using “out-of-the-box thinking” and code — combining design sense with technical implementation.
Nadieh’s background: she trained in astronomy, worked in data science, but found her passion in data visualization. Over time she developed a distinct style of data-driven “data art” rather than generic graphs.
Data → Story → Visual
A recurring theme: good visualizations start with a story — or a question — not just with data. You ask: what insight or narrative do you want to reveal? That shapes how you approach the data and what kind of visual you will build.
Nadieh emphasizes that often the best question emerges after a bit of data exploration — so the “question” evolves.
Before designing, you have to understand what the data actually contains: its type (quantitative, categorical, etc.), structure, quirks, what’s important — and who will be reading the visualization. This affects choices like chart type, level of detail, labels, readability.
Not all charts fit all data: pick a visual representation that expresses clearly what you want to communicate — trends, distributions, comparisons, relationships, etc. Sometimes that means abandoning “standard” charts in favor of more creative or custom visuals.
Because people are visual, design elements matter. But they must serve the data, not overshadow it. Use color thoughtfully (e.g., for differentiation, accessibility), maintain consistent palettes, use spacing, hierarchy, alignments to make it easy to read.
Nadieh’s own work often uses vibrant palettes and custom design touches — she argues that if you’re building by hand (e.g., using D3 + SVG), you can push beyond default library charts to create something truly expressive yet still accurate.
Why This Talk Stands Out
This talk offers something beyond standard data-viz best-practices or template-driven dashboards: it’s about treating data visualisation as a creative process, a blend of design, storytelling, and coding.
Seeing the creation from an empty browser to a full chart helps demystify the building process — it shows that you don’t need heavy software or prebuilt templates to produce something expressive and meaningful. You just need data, a clear purpose, and willingness to think visually + code.. This talk is inspiring to me on many levels because my research is based on creating meaningful data visualizations rather than just “reports” or “dashboards”
https://www.geeksforgeeks.org/data-visualization/6-tips-for-creating-effective-data-visualizations
