Useful tools and tutorials for learning data visualization.
- Jupyter Notebook - browser-based interactive platform for coding in Python.
- Observable Notebook - the notebook paradigm to JavaScript projects, like Jupyter but in JavaScript.
- A full example of visualization and analysis of a multidimensional dataset using D3 and Vega-Lite can be found in this notebook
- Tableau Public - Free version of Tableau for publishing visualizations on the web
- Tableau for Students - Free license for students using the desktop version of Tableau
- Vega-Lite - declarative visualization using JSON specifications
- Color Brewer
- Flat UI Colors
- Material UI Color
- D3-scale-chromatic
- Matplotlib colormaps
- Adobe Color Palette Generator
- D3.js - popular JavaScript visualization library
- Leafleat - a JavaScript library for interactive maps
- Mapbox - WebGL accelerated maps
- Matplotlib - popular Python low-level visualization library
- Altair - based on Vega-Lite but in Python.
- Seaborn - visualization libarary with more focus on statistics, built upon Matplotlib.
- Plotly - interactive Python visualization library, less customizable in terms of creating your own visualization.
- Dash - heavily relies on Plotly by default
Here are some tutorials that could be helpful for completing the assignments and final project.
- A re-introduction to JavaScript (Mozilla Developer Network)
- Eloquent JavasScript - free online book.
- ES6 JavaScript Tutorial - ECMAScript 2015, JavaScript version standardized in 2015.
- D3-Observable-Walkthrough - tutorial for using D3 js
- D3 Book - book on princples of D3
- Python Basics
- Scikit-Learn - a popular machine learning library
- Pandas - a powerful library that helps you data
- TypeScript - Typing JavaScript
- Typing in Python
(Credits: Keshav Dasu, Yun-Hsin Kuo)