Abstract
Davinci is an open-source Data Science project. Davinci is a DVsaaS (Data Visualization as a Service) Platform. Davinci is oriented towards product managers, business people, data engineers, data analysts, data scientists, etc. It aims to provide a one-stop data visualization solution, which could be both independently used as public cloud/private cloud and integrated into third-party systems as plugin. It is built using TypeScript, React. Key capabilities include: Data Source Support; Files in CSV format; JDBC data source. 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
Davinci is oriented towards product managers, business people, data engineers, data analysts, data scientists, etc. It aims to provide a one-stop data visualization solution, which could be both independently used as public cloud/private cloud and integrated into third-party systems as plugin. A simple configuration on Davinci UI can meet multiple visualization requirements. It also supports other visualization features like advanced interaction, industry analysis, pattern searching, social intelligence, etc.
2. Objective
Davinci is a DVsaaS (Data Visualization as a Service) Platform
This project demonstrates how TypeScript, React can be applied to a real-world Data Science problem.
3. Key Features / Modules
- Data Source Support
- Files in CSV format
- JDBC data source
- Data View Support
- Customized SQL template
- SQL highlighting
- SQL test
- WriteBack mode
- Visual Components Support
- Pre-defined charts
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Node.js (LTS) and npm / yarn / pnpm
- VS Code or any code editor
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/edp963/davinci.git
cd davinciFull 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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by edp963 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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