Rust Data Analysis

Abstract

1. Introduction

Welcome to the Rust Data Analysis repository! This collection of Jupyter notebooks provides a comprehensive exploration of data analysis using Rust. Powered by a Rust kernel, these notebooks allow you to dive deep into the realm of data analysis, leveraging the capabilities of the Rust programming language. With the help of various Rust libraries, such as ndarray, plotters, and more, you'll be able to extract valuable insights from different datasets with ease.

2. Objective

Rust for data analysis encyclopedia (WIP).

This project demonstrates how Jupyter Notebook, Rust can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

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

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Rust toolchain (rustup / cargo)
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/wiseaidev/rust-data-analysis.git
cd rust-data-analysis

1. Clone the repository to your local machine:
2. Install the required dependencies and libraries. Make sure you have Rust, Jupyter Notebook, and evcxr_jupyter installed on your system.
3. Navigate to the cloned repository:
4. Start Jupyter Notebook:
5. Access the notebooks in your web browser by clicking on the notebook file you want to explore.

git clone https://github.com/wiseaidev/rust-data-analysis.git

# Install a Rust toolchain (e.g. nightly):
	curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain nightly

	# Install Jupyter Notebook
	pip install notebook

	# Install evcxr_jupyter
	cargo install evcxr_jupyter
	evcxr_jupyter --install

cd rust-data-analysis

jupyter notebook

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 wiseaidev 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.