🚀 Data Science Mentorship Program 2.0

Welcome to this extensive Data Science Learning Repository! This collection represents a complete curriculum covering everything from basic programming to advanced machine learning concepts, machine learning operations, and advanced statistical analysis.

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📚 Repository Overview

This repository is structured to provide a systematic learning path through various aspects of data science, including:

• 🐍 Python Programming Fundamentals
• 📊 Data Analysis & Visualization
• 🤖 Machine Learning Algorithms
• 📈 Statistical Analysis
• 🔧 Feature Engineering
• 🎯 Model Evaluation & Optimization
• 🔄 MLOps Practices

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📁 Repository Structure

.
├── Python/                      # Python Programming Fundamentals
│   ├── Introduction/
│   ├── Data Structures/
│   ├── OOP/
│   └── Advanced Topics/
│
├── Data Analysis/              # Data Analysis Fundamentals
├── Data Visualization/         # Visualization Libraries
│   ├── Matplotlib/
│   ├── Seaborn/
│   └── Plotly/
│
├── Machine Learning/           # ML Algorithms & Techniques
│   ├── Linear Models/
│   ├── Tree-Based Models/
│   ├── Clustering/
│   └── Dimensionality Reduction/
│
├── Feature Engineering/        # Feature Processing
│   ├── Feature Encoding/
│   ├── Feature Scaling/
│   ├── Feature Selection/
│   └── Missing Values/
│
├── Model Evaluation/          # Model Assessment
├── Model Explainability/      # Model Interpretation
├── Hyperparameter Tuning/     # Model Optimization
│
├── Statistics/               # Statistical Analysis
├── Linear Algebra/          # Mathematical Foundations
├── SQL/                     # Database Operations
└── MLOps/                   # ML Operations

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📂 Key Directories

• Python/: Fundamentals including OOP, functions, data structures, and more
• Numpy/: Array operations, mathematical functions, and data manipulation
• Pandas/: Data analysis, cleaning, and time series manipulation
• Data Visualization/:
  • Matplotlib: Static visualizations
  • Seaborn: Statistical data visualization
  • Plotly: Interactive plots and dashboards
• Machine Learning/:
  • Classical algorithms (Linear Regression, SVM, KNN)
  • Ensemble methods (Random Forest, XGBoost, LightGBM)
  • Clustering techniques (KMeans, DBSCAN, Hierarchical)
  • Dimensionality reduction (PCA, T-SNE)
• Feature Engineering/: Feature scaling, encoding, transformation techniques
• Statistics/: Statistical analysis and probability
• SQL/: Database operations and query optimization
• MLOps/: Machine learning operations and deployment
• Model Evaluation/: Metrics, validation techniques
• Model Explainability/: Model interpretation and analysis

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🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/Shriram-Vibhute/CampusX-DSMP2.0
   cd CampusX-DSMP2.0

2. Install dependencies:

   pip install -r requirements.txt

3. Explore the notebooks and exercises in each module.

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🛠️ Frameworks & Tools

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📚 Contributing

Contributions are welcome! Please open an issue or submit a pull request for improvements or suggestions.

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📄 License

This project is licensed under the MIT License.

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Happy Learning! 🚀