How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Visualization Curriculum is a curated collection of educational Jupyter notebooks and interactive lessons designed to teach foundational principles and practical design patterns for data visualization. The curriculum covers core concepts such as visual encoding, graphical marks, scales, data transformation, multi-view composition, and interaction techniques.
The main features of uwdata/visualization-curriculum are: Jupyter Notebook Curricula, Declarative Visualization Grammars, Interactive Data Visualizations, Cloud Execution Environments, Computational Notebooks, Curated Visualization Guides, Data Visualization Curricula, Interactive Learning Platforms.
Open-source alternatives to uwdata/visualization-curriculum include: bloomberg/bqplot — bqplot is an interactive data visualization library for Jupyter notebooks. It implements a grammar of graphics model,… mitdeeplearning/introtodeeplearning — This repository contains the lab materials and Jupyter notebooks for MIT's introductory deep learning course, using… vega/vega-lite — Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a… biolab/orange3 — Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows… mlnlp-world/deeplearning-muli-notes — This project is a deep learning study resource and educational curriculum designed for mastering neural network… ageron/tf2_course — This project is an educational resource consisting of a structured curriculum of interactive notebooks designed to…
bqplot is an interactive data visualization library for Jupyter notebooks. It implements a grammar of graphics model, allowing users to build complex 2D charts by combining marks, scales, and axes. The library distinguishes itself with specialized toolkits for financial charting, such as OHLC candlesticks and time-series analysis, and geographic data visualization, including choropleths and custom map projections for TopoJSON and GeoJSON data. It enables deep interaction through tools like lasso selection, rectangular brushing, and the ability to manually manipulate plot points or line data.
This repository contains the lab materials and Jupyter notebooks for MIT's introductory deep learning course, using TensorFlow and Keras for hands-on exercises. The courseware is delivered as pre-configured notebooks that run on Google Colaboratory's cloud infrastructure, eliminating the need for local software installation. Learners can toggle the Colab runtime to a GPU-backed hardware accelerator for faster neural network training during lab exercises. A shared Python package provides helper functions that standardize common operations across all notebooks. The course guides students throug
Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a concise JSON specification into a full Vega visualization, automatically inferring scales, axes, and legends from encoding declarations. The grammar-of-graphics encoding maps data fields to visual channels such as position, color, size, and shape, while a multi-view composition grammar enables layered, faceted, concatenated, and repeated layouts. Reactive parameter binding links named parameters to input widgets, selections, and expressions for dynamic updates. The project suppo
Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The