🧠 Data Science Basics Bootcamp

Welcome to the Data Science Basics Bootcamp, a beginner-friendly, hands-on introduction to data science! This bootcamp is designed for individuals with little to no experience in data analytics and provides a structured pathway to learn Python, data wrangling, EDA, visualization, inferential statistics, and machine learning.

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📋 Table of Contents

• 📅 Bootcamp Schedule
• 📁 Folder Structure
• 🚀 Getting Started
• 🛠️ Requirements
• 📚 Resources
• 📜 License

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📅 Bootcamp Schedule

Day	Topic	Slide	Code
Day 1	Python Recap & Pandas Basics	📑 Slides	📓 Notebooks
Day 2	Exploratory Data Analysis (EDA)	📑 Slides	📓 Notebooks
Day 3	Data Visualization	📑 Slides	📓 Notebooks
Day 4	Inferential Statistics	📑 Slides	📓 Notebooks
Day 5	Introduction to Machine Learning	📑 Slides	📓 Notebooks
Day 6	End-to-End ML Project	📑 Slides	📓 Notebooks

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📁 Folder Structure

Data-Science-Basics-Bootcamp/
│
├── syllabus/                   # PDF syllabus of the bootcamp
├── day1_python_intro/          # Python & Pandas introduction notebooks
├── day2_eda/                   # EDA-focused notebooks and projects
├── day3_visualization/         # Data visualization examples
├── day4_statistics/            # Inferential statistics content
├── day5_machine_learning/      # ML basics and model building
├── day6_end_to_end_ml/         # Full pipeline implementation
├── datasets/                   # Datasets used across the bootcamp
├── presentations/              # Slide decks used in the sessions
├── requirements.txt            # Python dependencies
└── README.md                   # You're here!

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🚀 Getting Started

✅ Option 1: Open in Google Colab (Recommended)

1. Navigate to any .ipynb file in this repo.
2. Click the "Open in Colab" badge or open via:
   https://colab.research.google.com/github/parhamzm/Data-Science-Basics-Bootcamp/blob/main/path/to/notebook.ipynb

🖥️ Option 2: Run Locally

1. Clone the repo:

git clone https://github.com/parhamzm/Data-Science-Basics-Bootcamp.git
cd Data-Science-Basics-Bootcamp

2. Install required packages:

pip install -r requirements.txt

3. Launch Jupyter or VS Code and start exploring the notebooks.

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🛠️ Requirements

This bootcamp uses the following Python libraries:

pandas
numpy
matplotlib
seaborn
scikit-learn

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📚 Learning Resources

Here are some supplemental resources we recommend for further learning:

• 📘 Python Data Science Handbook
• 📊 Seaborn Documentation
• 🧪 Scikit-learn Tutorials
• 📈 From Data to Viz
• 🎓 Kaggle Python & ML Courses

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📜 License

This project is licensed under the MIT License.
You are free to use, adapt, and share it with attribution.

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🙌 Acknowledgements

Thanks to all participants, contributors, and learners.
Let’s make data science more accessible to everyone!