Vizzu Lib

Library for animated data visualizations and data stories.

Data ScienceJavaScriptApache-2.0

Abstract

Vizzu Lib is an open-source Data Science project. Library for animated data visualizations and data stories. Vizzu - Library for animated data visualizations and data stories. It is built using JavaScript. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

Vizzu - Library for animated data visualizations and data stories.

2. Objective

Library for animated data visualizations and data stories.

This project demonstrates how JavaScript can be applied to a real-world Data Science problem.

4. Technology Stack

JavaScript

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Node.js (LTS) and npm
  • A modern web browser
  • VS Code or any code editor
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/vizzuhq/vizzu-lib.git
cd vizzu-lib
npm install vizzu
<html>
 <head>
  <script type="module">
   import Vizzu from 'https://cdn.jsdelivr.net/npm/vizzu@latest/dist/vizzu.min.js';
  </script>
 </head>
</html>
<html>
 <body>




 </body>
</html>
import Vizzu from 'https://cdn.jsdelivr.net/npm/vizzu@latest/dist/vizzu.min.js';

let data = {
    series: [{
        name: 'Foo',
        values: ['Alice', 'Bob', 'Ted']
    }, {
        name: 'Bar',
        values: [15, 32, 12]
    }, {
        name: 'Baz',
        values: [5, 3, 2]
    }]
};

let chart = new Vizzu('myVizzu', {
    data
});

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by vizzuhq and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.

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