🧠 Data Science Learning Lab

A hands-on, beginner-friendly repository for learning Python, Data Analysis, and Machine Learning through well-documented, practical Jupyter notebooks.

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🎯 About This Repository

This repository serves as a learning journey through the entire data science pipeline, starting from Python basics and advancing through data analysis, visualization, and machine learning.

Each folder focuses on a different stage of the learning process — containing clear, structured notebooks with step-by-step explanations, visual examples, and practice datasets.

Whether you're a beginner exploring Python, a student learning data analysis, or a self-learner aiming to master machine learning, this repo is your open notebook to follow along, experiment, and grow.

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🧩 Learning Path Overview

🐍 1. Python Foundations

Learn programming fundamentals essential for any data scientist.

• Variables, data types, and operators
• Control flow, loops, and functions
• String and dictionary manipulation
• Basic OOP concepts

📁 Folder: python/
📘 Example notebooks: python 101.ipynb, Python 102.ipynb, String manipulation.ipynb

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🐼 2. Data Analysis with Pandas

Master data cleaning, exploration, and transformation.

• Working with DataFrames and Series
• Data loading and inspection
• Missing values and outlier handling
• Grouping, aggregation, and pivot tables
• Data wrangling and reshaping (wide ↔ long)

📁 Folder: pandas/
📘 Example notebooks: Pandas 101.ipynb, data manipulation.ipynb

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📊 3. Data Visualization

Learn to communicate insights effectively with visuals.

• Static plots with Matplotlib
• Aesthetic and statistical plots with Seaborn
• Annotating and customizing charts
• Combining visuals for storytelling

📁 Folders:

• matplotlib/ → matplotlib 101.ipynb
• seaborn/ → seaborn 101.ipynb

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🤖 4. Machine Learning

Build predictive models and understand how machines learn from data.

Includes beginner-friendly notebooks covering:

• Data preprocessing and pipelines
• Linear regression from scratch
• Decision Trees and Ensemble Models
• Neural Networks, CNNs, and RNNs
• MNIST digit classification
• Text generation with RNNs

📁 Folder: Machine Learning/
📘 Example notebooks:
Intro_to_Neural_networks.ipynb, Decision Trees.ipynb, intro_to_CNNs.ipynb, Intro_to_RNNs.ipynb

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🚀 Getting Started

1️⃣ Prerequisites

• Python 3.8+
• Jupyter Notebook or JupyterLab
• Basic familiarity with programming

2️⃣ Installation

# Clone the repository
git clone https://github.com/Kibet-Rotich/data-science-learning.git
cd "DS Learning"

# (Optional) Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter Notebook
jupyter notebook

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📁 Repository Structure

DS Learning/
│
├── python/                  # Python fundamentals
│   ├── python 101.ipynb
│   ├── Python 102.ipynb
│   ├── String manipulation.ipynb
│   └── Dictionaries.ipynb
│
├── pandas/                  # Data analysis with Pandas
│   ├── Pandas 101.ipynb
│   ├── data manipulation.ipynb
│   ├── purchases.csv
│   └── purchases 2.csv
│
├── matplotlib/              # Data visualization with Matplotlib
│   └── matplotlib 101.ipynb
│
├── seaborn/                 # Visualization with Seaborn
│   └── seaborn 101.ipynb
│
├── Machine Learning/        # Machine learning notebooks
│   ├── ML_Pipeline.ipynb
│   ├── Decision Trees.ipynb
│   ├── Ensemble_learning.ipynb
│   ├── Intro_to_Neural_networks.ipynb
│   ├── intro_to_CNNs.ipynb
│   ├── Intro_to_RNNs.ipynb
│   └── MNIST project.ipynb
│
├── requirements.txt
└── README.md

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🧠 How to Learn with This Repo

👨‍💻 Self-Learners

1. Start from the Python folder
2. Progress through Pandas → Visualization → Machine Learning
3. Run each cell, read the comments, and experiment with your own data

🧑‍🏫 Educators

• Use these notebooks as classroom materials or lab guides
• Assign exercises or project sections for students
• Integrate into workshops or bootcamps

🤝 Study Groups

• Learn collaboratively through discussions
• Recreate visualizations or models together
• Share insights and practical projects

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🤝 Contributing

Contributions are welcome!
You can:

• Add new tutorials or examples
• Improve explanations or comments
• Suggest new datasets or topics
• Fix typos and enhance documentation

How to Contribute

1. Fork this repository
2. Create a branch (git checkout -b feature/add-topic)
3. Commit changes (git commit -m "Added linear regression notebook")
4. Push and open a Pull Request

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🧾 License

Licensed under the MIT License — free to use, modify, and share.

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💬 Feedback & Contact

Have ideas or suggestions? Reach out!

📧 Email: asbelkibetrotich@gmail.com
🌍 Connect on LinkedIn

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✨ Happy Learning, One Notebook at a Time! ✨

If you found this repository helpful, don't forget to ⭐ star it!