Jan 10, 2022 · This LibGuide collects resources and tutorials related to data visualization. It is a companion to the visualization services provided by Data and Visualization Services at Duke University Libraries. This is a gallery of common data visualization types that are general enough for many data sources.

May 7, 2024 · Interactive data visualization takes static data visualization a step further by allowing users to directly interact with the data itself. It gives users the ability to compare various datasets, dive down into specifics, engage with data in …

May 19, 2023 · 2D (Planar) data visualisation. This technique presents data in the form of images, diagrams, or charts on a plane surface. Cartogram and dot distribution map are examples of 2D data visualisation. Some tools used to create 2D data visualisation patterns are GeoCommons, Google Fusion Tables, Google Maps API, Polymaps, Tableau Public, etc.

Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources

Apr 15, 2023 · In this article, we will explore 1D, 2D, and 3D visualization techniques along with their Python implementation. 1D visualization plots are used to represent a single variable. Some of...

Feb 3, 2025 · The taxonomy is heavily weighted toward the more abstract information visualization techniques and is less representative of scientific visualizations, which can be highly specialized by domain and are more difficult to generalize. 1D/Linear; 2D/Planar (incl. Geospatial) 3D/Volumetric; Temporal; nD/Multidimensional; Tree/Hierarchical; Network

Aug 4, 2023 · The types of data visualizations that we defined in this lesson include 1D (or linear data visualization, in which a list of data items are organized by a single criterion), 2D (or planar...

Dimensionality: 0D, 1D, 2D, 3D, 4D, ... Influence: How does a data point influence its neighborhood? Structure: Are data points connected? How? (Topology) Does it have any holes in it? Is it all connected together? Can it be separated into parts? Which data organization is optimal? Where do the data come from? Is there a neighborhood relationship?

Feb 19, 2025 · This guide from Duke University Libraries provides an instruction to common visualization types by data dimensionality: 1D/linear, 2D/planar, 3D/volumetric, temporal, nD/multidimensional, tree/hierarchical, and network.

Project nD data to 2D or 3D for viewing. Often used to interpret and sanity check high-dimensional representations fit by machine learning methods. Different DR methods make different trade-offs: for example to preserve global structure (e.g., PCA) or emphasize local structure (e.g., nearest-neighbor approaches, including t-SNE and UMAP).