Data-science-project-1-BLS-Coders-

📊 Data Science Project 1 – BLS Coders

Welcome to the first project in our Data Science learning journey! This repository is part of the BLS Coders series, and it focuses on building a strong foundation in environment setup using the Conda ecosystem, including tools like Anaconda, Jupyter Notebook, and JupyterLab.

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🚀 Project Objective

This project helps you understand and configure a reproducible Python environment for data science tasks. It covers:

• Installing and configuring Anaconda or Miniconda
• Managing conda environments
• Installing essential data science packages
• Using JupyterLab as your primary development interface

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🛠️ Tools & Technologies

• Python (via Anaconda/Miniconda)
• Conda (Package & Environment Manager)
• Jupyter Notebook / JupyterLab
• Anaconda Navigator

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📦 Key Concepts Covered

✅ Installing Anaconda

• Step-by-step guide for Windows, macOS, and Linux
• Verifying installation via conda --version

🔄 Anaconda vs Miniconda

• Anaconda: Full-featured, beginner-friendly
• Miniconda: Lightweight, flexible for custom setups

🧪 Conda Workflow

• Creating and managing virtual environments
• Installing packages from channels like conda-forge
• Best practices: Always activate before install!

📁 Using Anaconda Navigator

• GUI for launching tools like JupyterLab
• Ideal for beginners avoiding command line

📓 Jupyter Notebook vs JupyterLab

Feature	Jupyter Notebook	JupyterLab
Interface	Single document	Multi-tab/panel
Customization	Limited	Extensive
Use Case	Tutorials/Quick	Full workflows

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📂 Folder Structure

├── environment/
│   └── conda_setup.md         # Environment setup instructions
├── notebooks/
│   └── sample_notebook.ipynb  # JupyterLab starter notebook
├── data/                      # (Optional) Raw or sample data files
├── README.md

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📝 How to Use This Repo

1. Clone the repo

   git clone https://github.com/Krushna-Chandra/Data-science-project-1-BLS-Coders-.git
   cd Data-science-project-1-BLS-Coders-
   

2. Install Anaconda or Miniconda Follow the steps in environment/conda_setup.md or the main notebook.

3. Create and activate a new environment

   conda create -n ds_env python=3.11
   conda activate ds_env
   

4. Install essential packages

   conda install -c conda-forge numpy pandas jupyterlab
   

5. Launch JupyterLab

   jupyter lab
   

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📚 Learning Outcome

By completing this setup and project, you will:

• Understand how to isolate Python environments using Conda
• Confidently install and manage libraries using conda-forge
• Use JupyterLab for interactive data science workflows
• Be ready to begin your first real-world data analysis project!

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🙌 Contributing

Pull requests are welcome. If you find a typo or want to add improvements, feel free to contribute.

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🧑‍💻 Author

Krushna Chandra Bindhani B.Tech (CSE), Final Year | Aspiring Data Scientist GitHub | LinkedIn

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📄 License

This project is open source and available under the MIT License.

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