Comprehensive Data Science Environment Setup

Table of Contents

This guide will walk you through setting up a complete data science environment with Anaconda, Jupyter Notebooks, Python, IntelliJ IDEA, R, and RStudio. By the end, you’ll have a powerful and flexible workspace for all your data science projects.

1. Installing Anaconda
#

Anaconda is a distribution of Python and R for scientific computing that simplifies package management and deployment.

Download and Install Anaconda
#

Visit the Anaconda download page and get the appropriate version for your platform. This guide focuses on Linux, but the process is similar for Windows and macOS.

# For Linux, after downloading the .sh file, make it executable:
chmod +x Anaconda3-2023.X-Linux-x86_64.sh

# Run the installer
./Anaconda3-2023.X-Linux-x86_64.sh

# Follow the prompts to complete installation
# Remember to say "yes" when asked if you want to initialize Anaconda3

After installation, restart your terminal or run:

source ~/.bashrc

2. Setting Up Jupyter Notebooks with Enhancements
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Install Conda Extensions for Jupyter
#

These extensions allow you to manage conda environments directly from Jupyter:

# Install nb_conda for environment management in Jupyter
conda install -c anaconda nb_conda

# If you need development features
conda install -c anaconda-nb-extensions/label/dev nb_conda

Install Jupyter Extensions
#

Jupyter extensions add useful features like code formatting, table of contents, and more:

# Install contrib extensions (collection of community-contributed extensions)
conda install -c conda-forge jupyter_contrib_nbextensions 

# Install configurator to enable/disable extensions via web interface
conda install -c conda-forge jupyter_nbextensions_configurator

Customize Jupyter Appearance
#

You can customize the look and feel of Jupyter notebooks:

# Install Jupyter themes
conda install -c conda-forge jupyterthemes

# Set a dark theme (chesterish in this example)
jt -t chesterish

# Reset to default theme (if needed)
jt -r

3. Setting Up IntelliJ IDEA for Python Development
#

IntelliJ IDEA with the Python plugin provides a powerful IDE for Python development.

Download and Install IntelliJ IDEA
#

Download IntelliJ IDEA from the official website
Extract the downloaded archive:
```
tar -xzf ideaIC-*.tar.gz
```
Navigate to the bin directory and run the setup script:
```
cd idea-IC-*/bin
./idea.sh
```
Follow the installation prompts
Make sure to select “Create Desktop Entry” for easy access

Configure Python Support in IntelliJ IDEA
#

Launch IntelliJ IDEA
Install the Python Community Edition Plugin:
- Go to File → Settings → Plugins
- Search for “Python Community Edition”
- Click Install and restart the IDE when prompted
Configure your Python/Conda environment:
- Go to File → Project Structure → SDKs
- Click the “+” button and select “Python SDK”
- Choose “Conda Environment” → “Existing environment”
- Navigate to your Anaconda installation (typically in ~/anaconda3)
- Or create a new virtual environment specific to your project

4. Setting Up R and RStudio Support
#

Install R Kernel for Jupyter
#

To use R within Jupyter notebooks:

# Install R and the IRKernel package
conda install -c r r-irkernel

Install RStudio (Optional)
#

If you prefer a dedicated R environment alongside Jupyter:

# For Ubuntu/Debian-based distributions:
wget https://download1.rstudio.org/desktop/bionic/amd64/rstudio-2023.X.X-XXX-amd64.deb
sudo dpkg -i rstudio-2023.X.X-XXX-amd64.deb
sudo apt-get install -f  # To resolve dependencies if needed

5. Launching Your Data Science Environment
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Starting Jupyter Notebook
#

# Start Jupyter Notebook server
jupyter notebook

Your browser will open with the Jupyter interface. You can now create new notebooks with either Python or R kernels.

Using Jupyter Extensions
#

In the Jupyter interface, navigate to the “Nbextensions” tab
Enable the extensions you want to use
Return to the “Files” tab to create or open notebooks

Working with Projects in IntelliJ IDEA
#

Launch IntelliJ IDEA
Select “New Project” or “Open”
Choose “Python” as the project type
Select your configured Python/Conda interpreter
Start developing your Python code with full IDE support

Troubleshooting
#

Common Issues
#

“Command not found” after installing Anaconda: Make sure to restart your terminal or source your .bashrc file.
Missing packages in Jupyter: Ensure you’ve activated the correct environment.
IntelliJ not recognizing Python: Verify your Python SDK configuration in Project Structure.

Environment Management
#

Keep track of your environments and installed packages:

# List all conda environments
conda env list

# List packages in current environment
conda list

# Export environment for sharing/backup
conda env export > environment.yml

# Create environment from exported file
conda env create -f environment.yml

This setup provides a complete data science environment with both GUI tools and command-line capabilities. You now have the flexibility to work with Python and R in both notebook and IDE formats, giving you the best of all worlds for data science projects.