Simple Data Analysis

Abstract

1. Introduction

Most users will want this package (simple-data-analysis), which includes all of the core functionality plus the extended features.

SDA is a fast and easy-to-use TypeScript library for tabular, geospatial, vector, and AI-assisted data analysis. It runs on Deno, Node.js, and Bun and makes it easy to load data from files, databases, and public sources.

contains all core functions that depend on DuckDB (data loading, filtering, joining, summarizing, geospatial operations, etc.). If you only need these core data analysis capabilities, you can use this lighter package directly.

2. Objective

Fast DuckDB-powered TypeScript library for tabular, geospatial, vector, AI, Google Sheets, and data visualization workflows on Deno, Node.js, and Bun.

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

4. Technology Stack

• simple-data-analysis-core
• simple-data-analysis (this package) extends the core with additional

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
• Node.js (LTS) and npm
• A modern web browser
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/nshiab/simple-data-analysis.git
cd simple-data-analysis

# Deno
deno add jsr:@nshiab/simple-data-analysis

# Node.js
npm i @nshiab/simple-data-analysis

# Bun
bun add @nshiab/simple-data-analysis

# Deno
deno run -A jsr:@nshiab/setup-data-project

# Node
npx @nshiab/setup-data-project

# Bun
bunx @nshiab/setup-data-project

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 nshiab and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.