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INSTALLATION.md

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Installation Guide

This guide will help you set up your environment to work with the Data Science for Beginners curriculum.

Table of Contents

Prerequisites

Before you begin, you should have:

  • Basic familiarity with command line/terminal
  • A GitHub account (free)
  • Stable internet connection for initial setup

Quick Start Options

Option 1: GitHub Codespaces (Recommended for Beginners)

The easiest way to get started is with GitHub Codespaces, which provides a complete development environment in your browser.

  1. Navigate to the repository
  2. Click the Code dropdown menu
  3. Select the Codespaces tab
  4. Click Create codespace on main
  5. Wait for the environment to initialize (2-3 minutes)

Your environment is now ready with all dependencies pre-installed!

Option 2: Local Development

For working on your own computer, follow the detailed instructions below.

Local Installation

Step 1: Install Git

Git is required to clone the repository and track your changes.

Windows:

  • Download from git-scm.com
  • Run the installer with default settings

macOS:

  • Install via Homebrew: brew install git
  • Or download from git-scm.com

Linux:

# Debian/Ubuntu
sudo apt-get update
sudo apt-get install git

# Fedora
sudo dnf install git

# Arch
sudo pacman -S git

Step 2: Clone the Repository

# Clone the repository
git clone https://github.com/microsoft/Data-Science-For-Beginners.git

# Navigate to the directory
cd Data-Science-For-Beginners

Step 3: Install Python and Jupyter

Python 3.7 or higher is required for the data science lessons.

Windows:

  1. Download Python from python.org
  2. During installation, check "Add Python to PATH"
  3. Verify installation:
python --version

macOS:

# Using Homebrew
brew install python3

# Verify installation
python3 --version

Linux:

# Most Linux distributions come with Python pre-installed
python3 --version

# If not installed:
# Debian/Ubuntu
sudo apt-get install python3 python3-pip

# Fedora
sudo dnf install python3 python3-pip

Step 4: Set Up Python Environment

It's recommended to use a virtual environment to keep dependencies isolated.

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate

# On macOS/Linux:
source venv/bin/activate

Step 5: Install Python Packages

Install the required data science libraries:

pip install jupyter pandas numpy matplotlib seaborn scikit-learn

Step 6: Install Node.js and npm (For Quiz App)

The quiz application requires Node.js and npm.

Windows/macOS:

  • Download from nodejs.org (LTS version recommended)
  • Run the installer

Linux:

# Debian/Ubuntu
# WARNING: Piping scripts from the internet directly into bash can be a security risk.
# It is recommended to review the script before running it:
#   curl -fsSL https://deb.nodesource.com/setup_lts.x -o setup_lts.x
#   less setup_lts.x
# Then run:
#   sudo -E bash setup_lts.x
#
# Alternatively, you can use the one-liner below at your own risk:
curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash -
sudo apt-get install -y nodejs

# Fedora
sudo dnf install nodejs

# Verify installation
node --version
npm --version

Step 7: Install Quiz App Dependencies

# Navigate to quiz app directory
cd quiz-app

# Install dependencies
npm install

# Return to root directory
cd ..

Step 8: Install Docsify (Optional)

For offline access to documentation:

npm install -g docsify-cli

Verify Your Installation

Test Python and Jupyter

# Activate your virtual environment if not already activated
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Start Jupyter Notebook
jupyter notebook

Your browser should open with the Jupyter interface. You can now navigate to any lesson's .ipynb file.

Test Quiz Application

# Navigate to quiz app
cd quiz-app

# Start development server
npm run serve

The quiz app should be available at http://localhost:8080 (or another port if 8080 is busy).

Test Documentation Server

# From the root directory of the repository
docsify serve

The documentation should be available at http://localhost:3000.

Using VS Code Dev Containers

If you have Docker installed, you can use VS Code Dev Containers:

  1. Install Docker Desktop
  2. Install Visual Studio Code
  3. Install the Remote - Containers extension
  4. Open the repository in VS Code
  5. Press F1 and select "Remote-Containers: Reopen in Container"
  6. Wait for the container to build (first time only)

Next Steps

Getting Help

If you encounter issues:

  1. Check the TROUBLESHOOTING.md guide
  2. Search existing GitHub Issues
  3. Join our Discord community
  4. Create a new issue with detailed information about your problem