Co-authored-by: leestott <2511341+leestott@users.noreply.github.com>copilot/fix-0de5e46c-afe2-43ab-8c38-67d5a3358ccc
copilot-swe-agent[bot]
2 months ago
parent
fc45572aa6
commit
3503f04860
@ -0,0 +1,239 @@
|
|||||||
|
# Installation Guide
|
||||||
|
|
||||||
|
This guide will help you set up your environment to work with the Data Science for Beginners curriculum.
|
||||||
|
|
||||||
|
## Table of Contents
|
||||||
|
|
||||||
|
- [Prerequisites](#prerequisites)
|
||||||
|
- [Quick Start Options](#quick-start-options)
|
||||||
|
- [Local Installation](#local-installation)
|
||||||
|
- [Verify Your Installation](#verify-your-installation)
|
||||||
|
|
||||||
|
## 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](https://github.com/microsoft/Data-Science-For-Beginners)
|
||||||
|
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](https://git-scm.com/download/win)
|
||||||
|
- Run the installer with default settings
|
||||||
|
|
||||||
|
**macOS:**
|
||||||
|
- Install via Homebrew: `brew install git`
|
||||||
|
- Or download from [git-scm.com](https://git-scm.com/download/mac)
|
||||||
|
|
||||||
|
**Linux:**
|
||||||
|
```bash
|
||||||
|
# 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
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 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](https://www.python.org/downloads/)
|
||||||
|
2. During installation, check "Add Python to PATH"
|
||||||
|
3. Verify installation:
|
||||||
|
```bash
|
||||||
|
python --version
|
||||||
|
```
|
||||||
|
|
||||||
|
**macOS:**
|
||||||
|
```bash
|
||||||
|
# Using Homebrew
|
||||||
|
brew install python3
|
||||||
|
|
||||||
|
# Verify installation
|
||||||
|
python3 --version
|
||||||
|
```
|
||||||
|
|
||||||
|
**Linux:**
|
||||||
|
```bash
|
||||||
|
# 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.
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 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:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
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](https://nodejs.org/) (LTS version recommended)
|
||||||
|
- Run the installer
|
||||||
|
|
||||||
|
**Linux:**
|
||||||
|
```bash
|
||||||
|
# Debian/Ubuntu
|
||||||
|
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
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 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:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
npm install -g docsify-cli
|
||||||
|
```
|
||||||
|
|
||||||
|
## Verify Your Installation
|
||||||
|
|
||||||
|
### Test Python and Jupyter
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 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
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 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
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 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](https://www.docker.com/products/docker-desktop)
|
||||||
|
2. Install [Visual Studio Code](https://code.visualstudio.com/)
|
||||||
|
3. Install the [Remote - Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
|
||||||
|
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
|
||||||
|
|
||||||
|
- Explore the [README.md](README.md) for an overview of the curriculum
|
||||||
|
- Read [USAGE.md](USAGE.md) for common workflows and examples
|
||||||
|
- Check [TROUBLESHOOTING.md](TROUBLESHOOTING.md) if you encounter issues
|
||||||
|
- Review [CONTRIBUTING.md](CONTRIBUTING.md) if you want to contribute
|
||||||
|
|
||||||
|
## Getting Help
|
||||||
|
|
||||||
|
If you encounter issues:
|
||||||
|
|
||||||
|
1. Check the [TROUBLESHOOTING.md](TROUBLESHOOTING.md) guide
|
||||||
|
2. Search existing [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues)
|
||||||
|
3. Join our [Discord community](https://aka.ms/ds4beginners/discord)
|
||||||
|
4. Create a new issue with detailed information about your problem
|
||||||
@ -0,0 +1,611 @@
|
|||||||
|
# Troubleshooting Guide
|
||||||
|
|
||||||
|
This guide provides solutions to common issues you might encounter while working with the Data Science for Beginners curriculum.
|
||||||
|
|
||||||
|
## Table of Contents
|
||||||
|
|
||||||
|
- [Python and Jupyter Issues](#python-and-jupyter-issues)
|
||||||
|
- [Package and Dependency Issues](#package-and-dependency-issues)
|
||||||
|
- [Jupyter Notebook Issues](#jupyter-notebook-issues)
|
||||||
|
- [Quiz Application Issues](#quiz-application-issues)
|
||||||
|
- [Git and GitHub Issues](#git-and-github-issues)
|
||||||
|
- [Docsify Documentation Issues](#docsify-documentation-issues)
|
||||||
|
- [Data and File Issues](#data-and-file-issues)
|
||||||
|
- [Performance Issues](#performance-issues)
|
||||||
|
- [Getting Additional Help](#getting-additional-help)
|
||||||
|
|
||||||
|
## Python and Jupyter Issues
|
||||||
|
|
||||||
|
### Python Not Found or Wrong Version
|
||||||
|
|
||||||
|
**Problem:** `python: command not found` or wrong Python version
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Check Python version
|
||||||
|
python --version
|
||||||
|
python3 --version
|
||||||
|
|
||||||
|
# If Python 3 is installed as 'python3', create an alias
|
||||||
|
# On macOS/Linux, add to ~/.bashrc or ~/.zshrc:
|
||||||
|
alias python=python3
|
||||||
|
alias pip=pip3
|
||||||
|
|
||||||
|
# Or use python3 explicitly
|
||||||
|
python3 -m pip install jupyter
|
||||||
|
```
|
||||||
|
|
||||||
|
**Windows Solution:**
|
||||||
|
1. Reinstall Python from [python.org](https://www.python.org/)
|
||||||
|
2. During installation, check "Add Python to PATH"
|
||||||
|
3. Restart your terminal/command prompt
|
||||||
|
|
||||||
|
### Virtual Environment Activation Issues
|
||||||
|
|
||||||
|
**Problem:** Virtual environment won't activate
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
**Windows:**
|
||||||
|
```bash
|
||||||
|
# If you get execution policy error
|
||||||
|
Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
|
||||||
|
|
||||||
|
# Then activate
|
||||||
|
venv\Scripts\activate
|
||||||
|
```
|
||||||
|
|
||||||
|
**macOS/Linux:**
|
||||||
|
```bash
|
||||||
|
# Ensure the activate script is executable
|
||||||
|
chmod +x venv/bin/activate
|
||||||
|
|
||||||
|
# Then activate
|
||||||
|
source venv/bin/activate
|
||||||
|
```
|
||||||
|
|
||||||
|
**Verify activation:**
|
||||||
|
```bash
|
||||||
|
# Your prompt should show (venv)
|
||||||
|
# Check Python location
|
||||||
|
which python # Should point to venv
|
||||||
|
```
|
||||||
|
|
||||||
|
### Jupyter Kernel Issues
|
||||||
|
|
||||||
|
**Problem:** "Kernel not found" or "Kernel keeps dying"
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Reinstall kernel
|
||||||
|
python -m ipykernel install --user --name=datascience --display-name="Python (Data Science)"
|
||||||
|
|
||||||
|
# Or use the default kernel
|
||||||
|
python -m ipykernel install --user
|
||||||
|
|
||||||
|
# Restart Jupyter
|
||||||
|
jupyter notebook
|
||||||
|
```
|
||||||
|
|
||||||
|
**Problem:** Wrong Python version in Jupyter
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
```bash
|
||||||
|
# Install Jupyter in your virtual environment
|
||||||
|
source venv/bin/activate # Activate first
|
||||||
|
pip install jupyter ipykernel
|
||||||
|
|
||||||
|
# Register the kernel
|
||||||
|
python -m ipykernel install --user --name=venv --display-name="Python (venv)"
|
||||||
|
|
||||||
|
# In Jupyter, select Kernel -> Change kernel -> Python (venv)
|
||||||
|
```
|
||||||
|
|
||||||
|
## Package and Dependency Issues
|
||||||
|
|
||||||
|
### Import Errors
|
||||||
|
|
||||||
|
**Problem:** `ModuleNotFoundError: No module named 'pandas'` (or other packages)
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Ensure virtual environment is activated
|
||||||
|
source venv/bin/activate # macOS/Linux
|
||||||
|
venv\Scripts\activate # Windows
|
||||||
|
|
||||||
|
# Install missing package
|
||||||
|
pip install pandas
|
||||||
|
|
||||||
|
# Install all common packages
|
||||||
|
pip install jupyter pandas numpy matplotlib seaborn scikit-learn
|
||||||
|
|
||||||
|
# Verify installation
|
||||||
|
python -c "import pandas; print(pandas.__version__)"
|
||||||
|
```
|
||||||
|
|
||||||
|
### Pip Installation Failures
|
||||||
|
|
||||||
|
**Problem:** `pip install` fails with permission errors
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Use --user flag
|
||||||
|
pip install --user package-name
|
||||||
|
|
||||||
|
# Or use virtual environment (recommended)
|
||||||
|
python -m venv venv
|
||||||
|
source venv/bin/activate
|
||||||
|
pip install package-name
|
||||||
|
```
|
||||||
|
|
||||||
|
**Problem:** `pip install` fails with SSL certificate errors
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Update pip first
|
||||||
|
python -m pip install --upgrade pip
|
||||||
|
|
||||||
|
# Try installing with trusted host (temporary workaround)
|
||||||
|
pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org package-name
|
||||||
|
```
|
||||||
|
|
||||||
|
### Package Version Conflicts
|
||||||
|
|
||||||
|
**Problem:** Incompatible package versions
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Create fresh virtual environment
|
||||||
|
python -m venv venv-new
|
||||||
|
source venv-new/bin/activate # or venv-new\Scripts\activate on Windows
|
||||||
|
|
||||||
|
# Install packages with specific versions if needed
|
||||||
|
pip install pandas==1.3.0
|
||||||
|
pip install numpy==1.21.0
|
||||||
|
|
||||||
|
# Or let pip resolve dependencies
|
||||||
|
pip install jupyter pandas numpy matplotlib seaborn scikit-learn
|
||||||
|
```
|
||||||
|
|
||||||
|
## Jupyter Notebook Issues
|
||||||
|
|
||||||
|
### Jupyter Won't Start
|
||||||
|
|
||||||
|
**Problem:** `jupyter notebook` command not found
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Install Jupyter
|
||||||
|
pip install jupyter
|
||||||
|
|
||||||
|
# Or use python -m
|
||||||
|
python -m jupyter notebook
|
||||||
|
|
||||||
|
# Add to PATH if needed (macOS/Linux)
|
||||||
|
export PATH="$HOME/.local/bin:$PATH"
|
||||||
|
```
|
||||||
|
|
||||||
|
### Notebook Won't Load or Save
|
||||||
|
|
||||||
|
**Problem:** "Notebook failed to load" or save errors
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
1. Check file permissions
|
||||||
|
```bash
|
||||||
|
# Make sure you have write permissions
|
||||||
|
ls -l notebook.ipynb
|
||||||
|
chmod 644 notebook.ipynb # If needed
|
||||||
|
```
|
||||||
|
|
||||||
|
2. Check for file corruption
|
||||||
|
```bash
|
||||||
|
# Try opening in text editor to check JSON structure
|
||||||
|
# Copy content to new notebook if corrupted
|
||||||
|
```
|
||||||
|
|
||||||
|
3. Clear Jupyter cache
|
||||||
|
```bash
|
||||||
|
jupyter notebook --clear-cache
|
||||||
|
```
|
||||||
|
|
||||||
|
### Cell Won't Execute
|
||||||
|
|
||||||
|
**Problem:** Cell stuck on "In [*]" or takes forever
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
1. **Interrupt the kernel**: Click "Interrupt" button or press `I, I`
|
||||||
|
2. **Restart kernel**: Kernel menu → Restart
|
||||||
|
3. **Check for infinite loops** in your code
|
||||||
|
4. **Clear output**: Cell → All Output → Clear
|
||||||
|
|
||||||
|
### Plots Not Displaying
|
||||||
|
|
||||||
|
**Problem:** `matplotlib` plots don't show in notebook
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```python
|
||||||
|
# Add magic command at the top of notebook
|
||||||
|
%matplotlib inline
|
||||||
|
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
|
||||||
|
# Create plot
|
||||||
|
plt.plot([1, 2, 3, 4])
|
||||||
|
plt.show() # Make sure to call show()
|
||||||
|
```
|
||||||
|
|
||||||
|
**Alternative for interactive plots:**
|
||||||
|
```python
|
||||||
|
%matplotlib notebook
|
||||||
|
# Or
|
||||||
|
%matplotlib widget
|
||||||
|
```
|
||||||
|
|
||||||
|
## Quiz Application Issues
|
||||||
|
|
||||||
|
### npm install Fails
|
||||||
|
|
||||||
|
**Problem:** Errors during `npm install`
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Clear npm cache
|
||||||
|
npm cache clean --force
|
||||||
|
|
||||||
|
# Remove node_modules and package-lock.json
|
||||||
|
rm -rf node_modules package-lock.json
|
||||||
|
|
||||||
|
# Reinstall
|
||||||
|
npm install
|
||||||
|
|
||||||
|
# If still failing, try with legacy peer deps
|
||||||
|
npm install --legacy-peer-deps
|
||||||
|
```
|
||||||
|
|
||||||
|
### Quiz App Won't Start
|
||||||
|
|
||||||
|
**Problem:** `npm run serve` fails
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Check Node.js version
|
||||||
|
node --version # Should be 12.x or higher
|
||||||
|
|
||||||
|
# Reinstall dependencies
|
||||||
|
cd quiz-app
|
||||||
|
rm -rf node_modules package-lock.json
|
||||||
|
npm install
|
||||||
|
|
||||||
|
# Try different port
|
||||||
|
npm run serve -- --port 8081
|
||||||
|
```
|
||||||
|
|
||||||
|
### Port Already in Use
|
||||||
|
|
||||||
|
**Problem:** "Port 8080 is already in use"
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Find and kill process on port 8080
|
||||||
|
# macOS/Linux:
|
||||||
|
lsof -ti:8080 | xargs kill -9
|
||||||
|
|
||||||
|
# Windows:
|
||||||
|
netstat -ano | findstr :8080
|
||||||
|
taskkill /PID <PID> /F
|
||||||
|
|
||||||
|
# Or use a different port
|
||||||
|
npm run serve -- --port 8081
|
||||||
|
```
|
||||||
|
|
||||||
|
### Quiz Not Loading or Blank Page
|
||||||
|
|
||||||
|
**Problem:** Quiz app loads but shows blank page
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
1. Check browser console for errors (F12)
|
||||||
|
2. Clear browser cache and cookies
|
||||||
|
3. Try a different browser
|
||||||
|
4. Ensure JavaScript is enabled
|
||||||
|
5. Check for ad blockers interfering
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Rebuild the app
|
||||||
|
npm run build
|
||||||
|
npm run serve
|
||||||
|
```
|
||||||
|
|
||||||
|
## Git and GitHub Issues
|
||||||
|
|
||||||
|
### Git Not Recognized
|
||||||
|
|
||||||
|
**Problem:** `git: command not found`
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
**Windows:**
|
||||||
|
- Install Git from [git-scm.com](https://git-scm.com/)
|
||||||
|
- Restart terminal after installation
|
||||||
|
|
||||||
|
**macOS:**
|
||||||
|
```bash
|
||||||
|
# Install via Homebrew
|
||||||
|
brew install git
|
||||||
|
|
||||||
|
# Or install Xcode Command Line Tools
|
||||||
|
xcode-select --install
|
||||||
|
```
|
||||||
|
|
||||||
|
**Linux:**
|
||||||
|
```bash
|
||||||
|
sudo apt-get install git # Debian/Ubuntu
|
||||||
|
sudo dnf install git # Fedora
|
||||||
|
```
|
||||||
|
|
||||||
|
### Clone Fails
|
||||||
|
|
||||||
|
**Problem:** `git clone` fails with authentication errors
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Use HTTPS URL
|
||||||
|
git clone https://github.com/microsoft/Data-Science-For-Beginners.git
|
||||||
|
|
||||||
|
# If you have 2FA enabled on GitHub, use Personal Access Token
|
||||||
|
# Create token at: https://github.com/settings/tokens
|
||||||
|
# Use token as password when prompted
|
||||||
|
```
|
||||||
|
|
||||||
|
### Permission Denied (publickey)
|
||||||
|
|
||||||
|
**Problem:** SSH key authentication fails
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Generate SSH key
|
||||||
|
ssh-keygen -t ed25519 -C "your_email@example.com"
|
||||||
|
|
||||||
|
# Add key to ssh-agent
|
||||||
|
eval "$(ssh-agent -s)"
|
||||||
|
ssh-add ~/.ssh/id_ed25519
|
||||||
|
|
||||||
|
# Add public key to GitHub
|
||||||
|
# Copy key: cat ~/.ssh/id_ed25519.pub
|
||||||
|
# Add at: https://github.com/settings/keys
|
||||||
|
```
|
||||||
|
|
||||||
|
## Docsify Documentation Issues
|
||||||
|
|
||||||
|
### Docsify Command Not Found
|
||||||
|
|
||||||
|
**Problem:** `docsify: command not found`
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Install globally
|
||||||
|
npm install -g docsify-cli
|
||||||
|
|
||||||
|
# If permission error on macOS/Linux
|
||||||
|
sudo npm install -g docsify-cli
|
||||||
|
|
||||||
|
# Verify installation
|
||||||
|
docsify --version
|
||||||
|
|
||||||
|
# If still not found, add npm global path
|
||||||
|
# Find npm global path
|
||||||
|
npm config get prefix
|
||||||
|
|
||||||
|
# Add to PATH (add to ~/.bashrc or ~/.zshrc)
|
||||||
|
export PATH="$PATH:/usr/local/bin"
|
||||||
|
```
|
||||||
|
|
||||||
|
### Documentation Not Loading
|
||||||
|
|
||||||
|
**Problem:** Docsify serves but content doesn't load
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Ensure you're in the repository root
|
||||||
|
cd Data-Science-For-Beginners
|
||||||
|
|
||||||
|
# Check for index.html
|
||||||
|
ls index.html
|
||||||
|
|
||||||
|
# Serve with specific port
|
||||||
|
docsify serve --port 3000
|
||||||
|
|
||||||
|
# Check browser console for errors (F12)
|
||||||
|
```
|
||||||
|
|
||||||
|
### Images Not Displaying
|
||||||
|
|
||||||
|
**Problem:** Images show broken link icon
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
1. Check image paths are relative
|
||||||
|
2. Ensure image files exist in the repository
|
||||||
|
3. Clear browser cache
|
||||||
|
4. Verify file extensions match (case-sensitive on some systems)
|
||||||
|
|
||||||
|
## Data and File Issues
|
||||||
|
|
||||||
|
### File Not Found Errors
|
||||||
|
|
||||||
|
**Problem:** `FileNotFoundError` when loading data
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```python
|
||||||
|
import os
|
||||||
|
|
||||||
|
# Check current working directory
|
||||||
|
print(os.getcwd())
|
||||||
|
|
||||||
|
# Use absolute path
|
||||||
|
data_path = os.path.join(os.getcwd(), 'data', 'filename.csv')
|
||||||
|
df = pd.read_csv(data_path)
|
||||||
|
|
||||||
|
# Or use relative path from notebook location
|
||||||
|
df = pd.read_csv('../data/filename.csv')
|
||||||
|
|
||||||
|
# Verify file exists
|
||||||
|
print(os.path.exists('data/filename.csv'))
|
||||||
|
```
|
||||||
|
|
||||||
|
### CSV Reading Errors
|
||||||
|
|
||||||
|
**Problem:** Errors reading CSV files
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```python
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
# Try different encodings
|
||||||
|
df = pd.read_csv('file.csv', encoding='utf-8')
|
||||||
|
# or
|
||||||
|
df = pd.read_csv('file.csv', encoding='latin-1')
|
||||||
|
# or
|
||||||
|
df = pd.read_csv('file.csv', encoding='ISO-8859-1')
|
||||||
|
|
||||||
|
# Handle missing values
|
||||||
|
df = pd.read_csv('file.csv', na_values=['NA', 'N/A', ''])
|
||||||
|
|
||||||
|
# Specify delimiter if not comma
|
||||||
|
df = pd.read_csv('file.csv', delimiter=';')
|
||||||
|
```
|
||||||
|
|
||||||
|
### Memory Errors with Large Datasets
|
||||||
|
|
||||||
|
**Problem:** `MemoryError` when loading large files
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
```python
|
||||||
|
# Read in chunks
|
||||||
|
chunk_size = 10000
|
||||||
|
chunks = []
|
||||||
|
for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):
|
||||||
|
# Process chunk
|
||||||
|
chunks.append(chunk)
|
||||||
|
df = pd.concat(chunks)
|
||||||
|
|
||||||
|
# Or read specific columns only
|
||||||
|
df = pd.read_csv('file.csv', usecols=['col1', 'col2'])
|
||||||
|
|
||||||
|
# Use more efficient data types
|
||||||
|
df = pd.read_csv('file.csv', dtype={'column_name': 'int32'})
|
||||||
|
```
|
||||||
|
|
||||||
|
## Performance Issues
|
||||||
|
|
||||||
|
### Slow Notebook Performance
|
||||||
|
|
||||||
|
**Problem:** Notebooks run very slowly
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
1. **Restart kernel and clear output**
|
||||||
|
- Kernel → Restart & Clear Output
|
||||||
|
|
||||||
|
2. **Close unused notebooks**
|
||||||
|
|
||||||
|
3. **Optimize code:**
|
||||||
|
```python
|
||||||
|
# Use vectorized operations instead of loops
|
||||||
|
# Bad:
|
||||||
|
result = []
|
||||||
|
for x in data:
|
||||||
|
result.append(x * 2)
|
||||||
|
|
||||||
|
# Good:
|
||||||
|
result = data * 2 # NumPy/Pandas vectorization
|
||||||
|
```
|
||||||
|
|
||||||
|
4. **Sample large datasets:**
|
||||||
|
```python
|
||||||
|
# Work with sample during development
|
||||||
|
df_sample = df.sample(n=1000) # or df.head(1000)
|
||||||
|
```
|
||||||
|
|
||||||
|
### Browser Crashes
|
||||||
|
|
||||||
|
**Problem:** Browser crashes or becomes unresponsive
|
||||||
|
|
||||||
|
**Solution:**
|
||||||
|
|
||||||
|
1. Close unused tabs
|
||||||
|
2. Clear browser cache
|
||||||
|
3. Increase browser memory (Chrome: `chrome://settings/system`)
|
||||||
|
4. Use JupyterLab instead:
|
||||||
|
```bash
|
||||||
|
pip install jupyterlab
|
||||||
|
jupyter lab
|
||||||
|
```
|
||||||
|
|
||||||
|
## Getting Additional Help
|
||||||
|
|
||||||
|
### Before Asking for Help
|
||||||
|
|
||||||
|
1. Check this troubleshooting guide
|
||||||
|
2. Search [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues)
|
||||||
|
3. Review [INSTALLATION.md](INSTALLATION.md) and [USAGE.md](USAGE.md)
|
||||||
|
4. Try searching the error message online
|
||||||
|
|
||||||
|
### How to Ask for Help
|
||||||
|
|
||||||
|
When creating an issue or asking for help, include:
|
||||||
|
|
||||||
|
1. **Operating System**: Windows, macOS, or Linux (which distribution)
|
||||||
|
2. **Python Version**: Run `python --version`
|
||||||
|
3. **Error Message**: Copy the complete error message
|
||||||
|
4. **Steps to Reproduce**: What you did before the error occurred
|
||||||
|
5. **What You've Tried**: Solutions you've already attempted
|
||||||
|
|
||||||
|
**Example:**
|
||||||
|
```
|
||||||
|
**Operating System:** macOS 12.0
|
||||||
|
**Python Version:** 3.9.7
|
||||||
|
**Error Message:** ModuleNotFoundError: No module named 'pandas'
|
||||||
|
**Steps to Reproduce:**
|
||||||
|
1. Activated virtual environment
|
||||||
|
2. Started Jupyter notebook
|
||||||
|
3. Tried to import pandas
|
||||||
|
|
||||||
|
**What I've Tried:**
|
||||||
|
- Ran pip install pandas
|
||||||
|
- Restarted Jupyter
|
||||||
|
```
|
||||||
|
|
||||||
|
### Community Resources
|
||||||
|
|
||||||
|
- **GitHub Issues**: [Create an issue](https://github.com/microsoft/Data-Science-For-Beginners/issues/new)
|
||||||
|
- **Discord**: [Join our community](https://aka.ms/ds4beginners/discord)
|
||||||
|
- **Discussions**: [GitHub Discussions](https://github.com/microsoft/Data-Science-For-Beginners/discussions)
|
||||||
|
- **Microsoft Learn**: [Q&A Forums](https://docs.microsoft.com/answers/)
|
||||||
|
|
||||||
|
### Related Documentation
|
||||||
|
|
||||||
|
- [INSTALLATION.md](INSTALLATION.md) - Setup instructions
|
||||||
|
- [USAGE.md](USAGE.md) - How to use the curriculum
|
||||||
|
- [CONTRIBUTING.md](CONTRIBUTING.md) - How to contribute
|
||||||
|
- [README.md](README.md) - Project overview
|
||||||
@ -0,0 +1,360 @@
|
|||||||
|
# Usage Guide
|
||||||
|
|
||||||
|
This guide provides examples and common workflows for using the Data Science for Beginners curriculum.
|
||||||
|
|
||||||
|
## Table of Contents
|
||||||
|
|
||||||
|
- [How to Use This Curriculum](#how-to-use-this-curriculum)
|
||||||
|
- [Working with Lessons](#working-with-lessons)
|
||||||
|
- [Working with Jupyter Notebooks](#working-with-jupyter-notebooks)
|
||||||
|
- [Using the Quiz Application](#using-the-quiz-application)
|
||||||
|
- [Common Workflows](#common-workflows)
|
||||||
|
- [Tips for Self-Learners](#tips-for-self-learners)
|
||||||
|
- [Tips for Teachers](#tips-for-teachers)
|
||||||
|
|
||||||
|
## How to Use This Curriculum
|
||||||
|
|
||||||
|
This curriculum is designed to be flexible and can be used in multiple ways:
|
||||||
|
|
||||||
|
- **Self-paced learning**: Work through lessons independently at your own speed
|
||||||
|
- **Classroom instruction**: Use as a structured course with guided instruction
|
||||||
|
- **Study groups**: Learn collaboratively with peers
|
||||||
|
- **Workshop format**: Intensive short-term learning sessions
|
||||||
|
|
||||||
|
## Working with Lessons
|
||||||
|
|
||||||
|
Each lesson follows a consistent structure to maximize learning:
|
||||||
|
|
||||||
|
### Lesson Structure
|
||||||
|
|
||||||
|
1. **Pre-lesson Quiz**: Test your existing knowledge
|
||||||
|
2. **Sketchnote** (Optional): Visual summary of key concepts
|
||||||
|
3. **Video** (Optional): Supplemental video content
|
||||||
|
4. **Written Lesson**: Core concepts and explanations
|
||||||
|
5. **Jupyter Notebook**: Hands-on coding exercises
|
||||||
|
6. **Assignment**: Practice what you've learned
|
||||||
|
7. **Post-lesson Quiz**: Reinforce your understanding
|
||||||
|
|
||||||
|
### Example Workflow for a Lesson
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 1. Navigate to the lesson directory
|
||||||
|
cd 1-Introduction/01-defining-data-science
|
||||||
|
|
||||||
|
# 2. Read the README.md
|
||||||
|
# Open README.md in your browser or editor
|
||||||
|
|
||||||
|
# 3. Take the pre-lesson quiz
|
||||||
|
# Click the quiz link in the README
|
||||||
|
|
||||||
|
# 4. Open the Jupyter notebook (if available)
|
||||||
|
jupyter notebook
|
||||||
|
|
||||||
|
# 5. Complete the exercises in the notebook
|
||||||
|
|
||||||
|
# 6. Work on the assignment
|
||||||
|
|
||||||
|
# 7. Take the post-lesson quiz
|
||||||
|
```
|
||||||
|
|
||||||
|
## Working with Jupyter Notebooks
|
||||||
|
|
||||||
|
### Starting Jupyter
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Activate your virtual environment
|
||||||
|
source venv/bin/activate # On macOS/Linux
|
||||||
|
# OR
|
||||||
|
venv\Scripts\activate # On Windows
|
||||||
|
|
||||||
|
# Start Jupyter from the repository root
|
||||||
|
jupyter notebook
|
||||||
|
```
|
||||||
|
|
||||||
|
### Running Notebook Cells
|
||||||
|
|
||||||
|
1. **Execute a cell**: Press `Shift + Enter` or click the "Run" button
|
||||||
|
2. **Execute all cells**: Select "Cell" → "Run All" from the menu
|
||||||
|
3. **Restart kernel**: Select "Kernel" → "Restart" if you encounter issues
|
||||||
|
|
||||||
|
### Example: Working with Data in a Notebook
|
||||||
|
|
||||||
|
```python
|
||||||
|
# Import required libraries
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
|
||||||
|
# Load a dataset
|
||||||
|
df = pd.read_csv('data/sample.csv')
|
||||||
|
|
||||||
|
# Explore the data
|
||||||
|
df.head()
|
||||||
|
df.info()
|
||||||
|
df.describe()
|
||||||
|
|
||||||
|
# Create a visualization
|
||||||
|
plt.figure(figsize=(10, 6))
|
||||||
|
plt.plot(df['column_name'])
|
||||||
|
plt.title('Sample Visualization')
|
||||||
|
plt.xlabel('X-axis Label')
|
||||||
|
plt.ylabel('Y-axis Label')
|
||||||
|
plt.show()
|
||||||
|
```
|
||||||
|
|
||||||
|
### Saving Your Work
|
||||||
|
|
||||||
|
- Jupyter auto-saves periodically
|
||||||
|
- Manually save: Press `Ctrl + S` (or `Cmd + S` on macOS)
|
||||||
|
- Your progress is saved in the `.ipynb` file
|
||||||
|
|
||||||
|
## Using the Quiz Application
|
||||||
|
|
||||||
|
### Running the Quiz App Locally
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Navigate to quiz app directory
|
||||||
|
cd quiz-app
|
||||||
|
|
||||||
|
# Start the development server
|
||||||
|
npm run serve
|
||||||
|
|
||||||
|
# Access at http://localhost:8080
|
||||||
|
```
|
||||||
|
|
||||||
|
### Taking Quizzes
|
||||||
|
|
||||||
|
1. Pre-lesson quizzes are linked at the top of each lesson
|
||||||
|
2. Post-lesson quizzes are linked at the bottom of each lesson
|
||||||
|
3. Each quiz has 3 questions
|
||||||
|
4. Quizzes are designed to reinforce learning, not to test exhaustively
|
||||||
|
|
||||||
|
### Quiz Numbering
|
||||||
|
|
||||||
|
- Quizzes are numbered 0-39 (40 total quizzes)
|
||||||
|
- Each lesson typically has a pre and post quiz
|
||||||
|
- Quiz URLs include the quiz number: `https://ff-quizzes.netlify.app/en/ds/quiz/0`
|
||||||
|
|
||||||
|
## Common Workflows
|
||||||
|
|
||||||
|
### Workflow 1: Complete Beginner Path
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 1. Set up your environment (see INSTALLATION.md)
|
||||||
|
|
||||||
|
# 2. Start with Lesson 1
|
||||||
|
cd 1-Introduction/01-defining-data-science
|
||||||
|
|
||||||
|
# 3. For each lesson:
|
||||||
|
# - Take pre-lesson quiz
|
||||||
|
# - Read the lesson content
|
||||||
|
# - Work through the notebook
|
||||||
|
# - Complete the assignment
|
||||||
|
# - Take post-lesson quiz
|
||||||
|
|
||||||
|
# 4. Progress through all 20 lessons sequentially
|
||||||
|
```
|
||||||
|
|
||||||
|
### Workflow 2: Topic-Specific Learning
|
||||||
|
|
||||||
|
If you're interested in a specific topic:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Example: Focus on Data Visualization
|
||||||
|
cd 3-Data-Visualization
|
||||||
|
|
||||||
|
# Explore lessons 9-13:
|
||||||
|
# - Lesson 9: Visualizing Quantities
|
||||||
|
# - Lesson 10: Visualizing Distributions
|
||||||
|
# - Lesson 11: Visualizing Proportions
|
||||||
|
# - Lesson 12: Visualizing Relationships
|
||||||
|
# - Lesson 13: Meaningful Visualizations
|
||||||
|
```
|
||||||
|
|
||||||
|
### Workflow 3: Project-Based Learning
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 1. Review the Data Science Lifecycle lessons (14-16)
|
||||||
|
cd 4-Data-Science-Lifecycle
|
||||||
|
|
||||||
|
# 2. Work through a real-world example (Lesson 20)
|
||||||
|
cd ../6-Data-Science-In-Wild/20-Real-World-Examples
|
||||||
|
|
||||||
|
# 3. Apply concepts to your own project
|
||||||
|
```
|
||||||
|
|
||||||
|
### Workflow 4: Cloud-Based Data Science
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Learn about cloud data science (Lessons 17-19)
|
||||||
|
cd 5-Data-Science-In-Cloud
|
||||||
|
|
||||||
|
# 17: Introduction to Cloud Data Science
|
||||||
|
# 18: Low-Code ML Tools
|
||||||
|
# 19: Azure Machine Learning Studio
|
||||||
|
```
|
||||||
|
|
||||||
|
## Tips for Self-Learners
|
||||||
|
|
||||||
|
### Stay Organized
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Create a learning journal
|
||||||
|
mkdir my-learning-journal
|
||||||
|
|
||||||
|
# For each lesson, create notes
|
||||||
|
echo "# Lesson 1 Notes" > my-learning-journal/lesson-01-notes.md
|
||||||
|
```
|
||||||
|
|
||||||
|
### Practice Regularly
|
||||||
|
|
||||||
|
- Set aside dedicated time each day or week
|
||||||
|
- Complete at least one lesson per week
|
||||||
|
- Review previous lessons periodically
|
||||||
|
|
||||||
|
### Engage with the Community
|
||||||
|
|
||||||
|
- Join the [Discord community](https://aka.ms/ds4beginners/discord)
|
||||||
|
- Participate in [GitHub Discussions](https://github.com/microsoft/Data-Science-For-Beginners/discussions)
|
||||||
|
- Share your progress and ask questions
|
||||||
|
|
||||||
|
### Build Your Own Projects
|
||||||
|
|
||||||
|
After completing lessons, apply concepts to personal projects:
|
||||||
|
|
||||||
|
```python
|
||||||
|
# Example: Analyze your own dataset
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
# Load your own data
|
||||||
|
my_data = pd.read_csv('my-project/data.csv')
|
||||||
|
|
||||||
|
# Apply techniques learned
|
||||||
|
# - Data cleaning (Lesson 8)
|
||||||
|
# - Exploratory data analysis (Lesson 7)
|
||||||
|
# - Visualization (Lessons 9-13)
|
||||||
|
# - Analysis (Lesson 15)
|
||||||
|
```
|
||||||
|
|
||||||
|
## Tips for Teachers
|
||||||
|
|
||||||
|
### Classroom Setup
|
||||||
|
|
||||||
|
1. Review [for-teachers.md](for-teachers.md) for detailed guidance
|
||||||
|
2. Set up a shared environment (GitHub Classroom or Codespaces)
|
||||||
|
3. Establish a communication channel (Discord, Slack, or Teams)
|
||||||
|
|
||||||
|
### Lesson Planning
|
||||||
|
|
||||||
|
**Suggested 10-Week Schedule:**
|
||||||
|
|
||||||
|
- **Week 1-2**: Introduction (Lessons 1-4)
|
||||||
|
- **Week 3-4**: Working with Data (Lessons 5-8)
|
||||||
|
- **Week 5-6**: Data Visualization (Lessons 9-13)
|
||||||
|
- **Week 7-8**: Data Science Lifecycle (Lessons 14-16)
|
||||||
|
- **Week 9**: Cloud Data Science (Lessons 17-19)
|
||||||
|
- **Week 10**: Real-World Applications & Final Projects (Lesson 20)
|
||||||
|
|
||||||
|
### Running Docsify for Offline Access
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Serve documentation locally for classroom use
|
||||||
|
docsify serve
|
||||||
|
|
||||||
|
# Students can access at localhost:3000
|
||||||
|
# No internet required after initial setup
|
||||||
|
```
|
||||||
|
|
||||||
|
### Assignment Grading
|
||||||
|
|
||||||
|
- Review student notebooks for completed exercises
|
||||||
|
- Check for understanding through quiz scores
|
||||||
|
- Evaluate final projects using data science lifecycle principles
|
||||||
|
|
||||||
|
### Creating Assignments
|
||||||
|
|
||||||
|
```python
|
||||||
|
# Example custom assignment template
|
||||||
|
"""
|
||||||
|
Assignment: [Topic]
|
||||||
|
|
||||||
|
Objective: [Learning goal]
|
||||||
|
|
||||||
|
Dataset: [Provide or have students find one]
|
||||||
|
|
||||||
|
Tasks:
|
||||||
|
1. Load and explore the dataset
|
||||||
|
2. Clean and prepare the data
|
||||||
|
3. Create at least 3 visualizations
|
||||||
|
4. Perform analysis
|
||||||
|
5. Communicate findings
|
||||||
|
|
||||||
|
Deliverables:
|
||||||
|
- Jupyter notebook with code and explanations
|
||||||
|
- Written summary of findings
|
||||||
|
"""
|
||||||
|
```
|
||||||
|
|
||||||
|
## Working Offline
|
||||||
|
|
||||||
|
### Download Resources
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Clone the entire repository
|
||||||
|
git clone https://github.com/microsoft/Data-Science-For-Beginners.git
|
||||||
|
|
||||||
|
# Download datasets in advance
|
||||||
|
# Most datasets are included in the repository
|
||||||
|
```
|
||||||
|
|
||||||
|
### Run Documentation Locally
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Serve with Docsify
|
||||||
|
docsify serve
|
||||||
|
|
||||||
|
# Access at localhost:3000
|
||||||
|
```
|
||||||
|
|
||||||
|
### Run Quiz App Locally
|
||||||
|
|
||||||
|
```bash
|
||||||
|
cd quiz-app
|
||||||
|
npm run serve
|
||||||
|
```
|
||||||
|
|
||||||
|
## Accessing Translated Content
|
||||||
|
|
||||||
|
Translations are available in 40+ languages:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Access translated lessons
|
||||||
|
cd translations/fr # French
|
||||||
|
cd translations/es # Spanish
|
||||||
|
cd translations/de # German
|
||||||
|
# ... and many more
|
||||||
|
```
|
||||||
|
|
||||||
|
Each translation maintains the same structure as the English version.
|
||||||
|
|
||||||
|
## Additional Resources
|
||||||
|
|
||||||
|
### Continue Learning
|
||||||
|
|
||||||
|
- [Microsoft Learn](https://docs.microsoft.com/learn/) - Additional learning paths
|
||||||
|
- [Student Hub](https://docs.microsoft.com/learn/student-hub) - Resources for students
|
||||||
|
- [Azure AI Foundry](https://aka.ms/foundry/forum) - Community forum
|
||||||
|
|
||||||
|
### Related Curricula
|
||||||
|
|
||||||
|
- [AI for Beginners](https://aka.ms/ai-beginners)
|
||||||
|
- [ML for Beginners](https://aka.ms/ml-beginners)
|
||||||
|
- [Web Dev for Beginners](https://aka.ms/webdev-beginners)
|
||||||
|
- [Generative AI for Beginners](https://aka.ms/genai-beginners)
|
||||||
|
|
||||||
|
## Getting Help
|
||||||
|
|
||||||
|
- Check [TROUBLESHOOTING.md](TROUBLESHOOTING.md) for common issues
|
||||||
|
- Search [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues)
|
||||||
|
- Join our [Discord](https://aka.ms/ds4beginners/discord)
|
||||||
|
- Review [CONTRIBUTING.md](CONTRIBUTING.md) to report issues or contribute
|
||||||
Loading…
Reference in new issue