Preface

Introduction

What is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines aspects of statistics, computer science, and domain knowledge to analyze and interpret complex data.

Why Use Rust for Data Science?

Rust is a systems programming language known for its performance, safety, and concurrency capabilities. These features make Rust an excellent choice for data science tasks, which often involve processing large datasets and performing computationally intensive operations. Rust's strong type system and memory safety guarantees can help reduce bugs and improve the reliability of data science applications.

Overview of the Book

This book aims to provide a comprehensive guide to using Rust for data science. It will cover everything from setting up the Rust environment and basic syntax to advanced topics like machine learning, deep learning, and data engineering. Each chapter will include practical examples and projects to help you apply the concepts learned.

Chapter_01

Rust Environment Setup

Rust is a modern and powerful systems programming language. To get started with Rust, you'll need to install it on your system.

This setup.md file provides instructions for setting up Rust on Windows, macOS, and Linux, as well as installing Rust for use with Jupyter Notebook using the evcxr tool. You can customize and expand this document as needed.

Here are instructions for various platforms:

Installing Rust on Windows

Visit the official Rust website for Windows: https://www.rust-lang.org/learn/get-started.
Download the rustup-init.exe installer.
Run the installer and follow the on-screen instructions.
Open a new command prompt or terminal window and type rustc --version to verify that Rust is installed.

Installing Rust on macOS

Open a terminal.
Install Homebrew if you don't already have it. Follow the instructions at https://brew.sh.
Install Rust using Homebrew:
To verify the installation, run rustc --version in the terminal.

Installing Rust on Linux

Open a terminal.
Visit the official Rust website for Linux: https://www.rust-lang.org/learn/get-started.
Follow the instructions for your Linux distribution. They typically involve running a command to install Rust using rustup.
To verify the installation, run rustc --version in the terminal.

Checking the Installation

After the installation has been completed successfully, you will have on your command line tool four new commands, you can check their versions as follows:

rustup: Rust installation tool manager
```
rustup --version
```
rustc: Rust Compiler
```
rustc --version
```
rustdoc: Rust documentation tool
```
rustdoc --version
```
cargo: Rust compilation and package manager
```
cargo --version
```

Setup Rust Kernel

Installing Jupyter Lab

You can skip this section if you already have jupyter lab (or notebook) installed on your machine. If not, you can choose one method to get things done. In the following subsection, I will focus on jupyter lab, the newer version of classic jupyter notebook.

Using anaconda distribution:
Using Command Line Tools: There is a chance that you don't want anaconda to be installed on you machine, in fact it takes a lot of space from the hard disk, especially the full version. If this is your case, then what you need is python to be installed, and then install jupyter lab. I assume you already have python, if not please do so.

Then it suffices only to run the following command to install jupyter lab:

pip install -U jupyterlab

Installing Jupyterlab Desktop Application

Unix-based Systems

Mac OS: Using brew utility as follows:
```
brew install jupyterlab
```
Linux (Ubuntu): You need to install snapd first
```
sudo apt update
sudo apt install snapd
```
Then you can simply use the following command:
```
sudo snap install jupyterlab-desktop --classic
```

Fedora Linux:

Install snapd
```
sudo dnf install snapd
```
Create symbolic link
```
sudo ln -s /var/lib/snapd/snap /snap
```

Install the application

sudo snap install jupyterlab-desktop --classic

Windows

winget install jupyterlab

Please check the jupyterlab official link if you have any problem with installation of you have a different operating system.

Installing Rust Kernel for Jupyter Notebook

If you want to use Rust in Jupyter Notebook, you can use the evcxr tool, which provides Rust support for Jupyter:

Install evcxr using cargo, the Rust package manager

cargo install evcxr_jupyter

Once evcxr is installed, you can configure Jupyter Notebook to use it

evcxr_jupyter --install

Start Jupyter Notebook
Create a new Jupyter notebook and choose the "Rust" kernel to start writing Rust code.

Now you're all set to explore the power of Rust on Jupyter Notebook.

Updating Rust

Updating Rust on different platforms is streamlined by the use of rustup command, the Rust’s official toolchain installer and manager.

The Rustup provides a uniform way to manage Rust versions across various environments. To update Rust using Rustup, open your terminal or command prompt and run:

rustup update

This command checks for the latest stable version of Rust, downloads it, and updates your system to use it. It's applicable to Windows, Linux, and macOS.

After Updating

After updating, you can verify the installation and check the current version by running:

rustc --version

This command will display the version of Rust currently installed, ensuring that your update was successful.

Uninstall Rust

Rust can be removed or uninstalled from the machine using the rustup manager.

On Mac os Machines

rustup self uninstall

Or you can uninstall rust using brew utility by typing the following command on a terminal application:

brew uninstall rust

Uninstalling Jupyter Rust Kernel

evcxr_jupyter --uninstall
cargo uninstall evcxr_jupyter

Introduction to Data Reading

Reading Plain Text Data

Reading CSV Files

Reading Text Data

Reading Excel Data

Connecting to Databases

Introduction to Data Preprocessing

Data Cleaning

Handling Missing Values

Data Transformation

Feature Scaling

Encoding Categorical Variables

Splitting Data into Training and Testing Sets

Introduction to Data Exploration

Descriptive Statistics

Data Visualization

Exploratory Data Analysis (EDA)

Correlation Analysis

Pandas-like Operations with DataFrames

Introduction to Machine Learning

Supervised Learning

Linear Regression

Logistic Regression

Decision Trees

Random Forests

Support Vector Machines (SVMs)

Unsupervised Learning

Clustering (K-Means, DBSCAN)

Dimensionality Reduction (PCA, LDA)

Model Evaluation and Validation

Introduction to Deep Learning

Neural Networks

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Training Deep Learning Models

Model Deployment

Time Series Analysis

Natural Language Processing (NLP)

Reinforcement Learning

Big Data Processing

Case Study 1: Predictive Analytics

Case Study 2: Image Classification

Case Study 3: Sentiment Analysis

Overview of Rust Libraries for Data Science

Using Polars for DataFrame Operations

Machine Learning with SmartCore

Deep Learning with TensorFlow Rust

Code Optimization

Memory Management

Debugging and Profiling

Documentation and Testing

Summary and Future Directions

Data Science Distilled with Rust