📊 Data Science Learning Journey

Master Data Science from Fundamentals to Machine Learning

🚀 Get Started • 📚 Curriculum • 🎯 Projects • 💡 Skills • 🤝 Connect

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🌟 About This Course

This repository is a comprehensive, hands-on data science curriculum designed to take you from absolute beginner to proficient data scientist. With 100+ Jupyter notebooks, real-world projects, and structured learning paths, you'll build a strong foundation in:

• ✅ Python Programming - Master the language of data science
• ✅ Data Analysis & Manipulation - Work with NumPy and Pandas
• ✅ Data Visualization - Create stunning charts and insights
• ✅ Web Scraping - Collect data from any website
• ✅ SQL Databases - Query and manage data efficiently
• ✅ Statistics & Probability - Build ML foundations
• ✅ Machine Learning - Train your first ML models

📈 Status: 🟢 Active & Growing - New content added regularly!

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📚 Curriculum

📖 Complete Course Modules

No.	Module	Topics Covered	Content	Skills
01	🎓 Data Science Intro	Tools, Environment Setup, Career Paths, DS Lifecycle	1 PDF Guide	Foundation setup
02	🐍 Python Fundamentals	Variables, Data Types, Operators, Control Flow, Loops, Data Structures, OOP, Lambda	18 Notebooks	Complete Python
03	🚀 Project: Social Network	Recommendation Algorithms, Graph Theory, JSON Processing	3 Notebooks	Real-world application
04	🔢 NumPy Mastery	Arrays, Indexing, Slicing, Broadcasting, Vectorization	5 Notebooks	Numerical computing
05	🐼 Pandas Deep Dive	DataFrames, Series, Grouping, Merging, Time Series	2 Notebooks	Data manipulation
06	📊 Data Visualization	Line, Bar, Pie, Scatter, Histogram, Heatmaps, Seaborn	8 Notebooks	Visual storytelling
07	🕷️ Web Scraping	HTTP Requests, HTML Parsing, BeautifulSoup, Data Extraction	2 Notebooks + 49 HTML samples	Web data collection
08	🗄️ SQL & Databases	CRUD Operations, Joins, Subqueries, Views, Stored Procedures	20 Tutorials	Database management
09	📈 Probability & Stats	Conditional Probability, Bayes Theorem, Distributions	3 Tutorials + Practice	Statistical thinking
10	🤖 ML Introduction	How Machines Learn, ML History, Traditional vs ML	PPT + Notes	ML fundamentals
11	🔧 Sklearn Basics	First ML Models, Training, Prediction, Model Selection	3 Notebooks	Scikit-learn
12	📋 ML Algorithm Types	Supervised vs Unsupervised Learning, Use Cases	3 Guides	Algorithm selection
13	🎯 ML Practice	Iris Classification, Model Evaluation, RMSE, MAE, Test Sets	5+ Notebooks	End-to-end ML

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🚀 Quick Start

Prerequisites

• 💻 Basic computer skills
• 🧠 Curiosity and willingness to learn
• ⏰ 8-10 hours per week commitment
• ❌ No prior programming experience needed!

Installation

Step 1: Clone the repository

git clone https://github.com/ggauravky/Data-Science-Learning.git
cd Data-Science-Learning

Step 2: Set up Python environment

# Option A: Using Conda (Recommended)
conda create -n datasci python=3.11 -y
conda activate datasci
conda install numpy pandas matplotlib seaborn jupyter scikit-learn -y
pip install beautifulsoup4 requests

# Option B: Using pip
pip install numpy pandas matplotlib seaborn jupyter beautifulsoup4 requests scikit-learn

Step 3: Launch Jupyter

jupyter notebook

Step 4: Start learning! 🎉

Navigate to 002 Python refresher/01_python_basic.ipynb and begin your journey!

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📖 Learning Path

🎯 Recommended 12-Week Roadmap

graph LR
    A[Week 1-2: Python] --> B[Week 3-4: NumPy & Pandas]
    B --> C[Week 5-6: Visualization]
    C --> D[Week 7: Web Scraping]
    D --> E[Week 8: SQL]
    E --> F[Week 9-10: Probability]
    F --> G[Week 11-12: Machine Learning]

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📅 Week-by-Week Breakdown (Click to expand)

🌱 Phase 1: Foundation (Weeks 1-4)

Week 1-2: Python Programming

• Complete all 18 Python notebooks
• Focus: Variables, loops, functions, OOP
• Practice: Daily coding exercises
• Milestone: Build a simple calculator app

Week 3: NumPy

• Master array operations
• Learn vectorization techniques
• Practice: Matrix manipulations

Week 4: Pandas & First Project

• DataFrame operations
• Data cleaning techniques
• Project: Coders of Delhi recommendation system

🌿 Phase 2: Intermediate (Weeks 5-8)

Week 5-6: Data Visualization

• All chart types in Matplotlib
• Statistical plots with Seaborn
• Practice: Visualize real datasets

Week 7: Web Scraping

• HTTP requests and responses
• HTML parsing with BeautifulSoup
• Project: Book scraper

Week 8: SQL Databases

• CRUD operations
• Complex joins and queries
• Practice: Build a movie database

🌳 Phase 3: Advanced (Weeks 9-12)

Week 9-10: Statistics & SQL Advanced

• Probability distributions
• Bayes theorem applications
• Stored procedures and optimization

Week 11-12: Machine Learning

• ML fundamentals
• First models with Scikit-learn
• Project: Iris classification
• Model evaluation and metrics

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🎯 Projects

Featured Real-World Projects

🌐 Coders of Delhi Social Network Recommendation System Build algorithms similar to Facebook's "People You May Know" feature. Tech Stack: Python, JSON, Graph Algorithms Complexity: Intermediate Skills: Data structures, algorithms, recommendation engines Files: data_read.ipynb people_you_may_know.ipynb pages_you_might_like.ipynb	📚 Book Data Scraper Web Scraping Pipeline Scrape 49 pages of book data from an online bookstore. Tech Stack: Requests, BeautifulSoup, Pandas Complexity: Beginner-Intermediate Skills: HTTP, HTML parsing, data extraction Output: Structured CSV with titles, prices, ratings
🌸 Iris Classification Machine Learning Project Train and evaluate ML models on the classic Iris dataset. Tech Stack: Scikit-learn, NumPy, Pandas Complexity: Intermediate Skills: Model training, evaluation, accuracy metrics Notebooks: Quick training Accuracy measurement Data analysis Test set creation Stratified sampling	📊 Data Analysis Suite Pandas Practice Projects Analyze real-world datasets with advanced techniques. Tech Stack: Pandas, Matplotlib, Seaborn Complexity: Beginner-Intermediate Skills: Grouping, merging, aggregation, visualization Features: Data cleaning pipelines Statistical analysis Trend visualization

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💡 Skills You'll Gain

🐍 Programming ✅ Python syntax & semantics ✅ Object-oriented programming ✅ Functional programming ✅ List comprehensions ✅ Lambda expressions ✅ File I/O operations ✅ JSON data handling ✅ Error handling	📊 Data Science ✅ NumPy array operations ✅ Pandas DataFrames ✅ Data cleaning & preprocessing ✅ Statistical analysis ✅ Data visualization ✅ Exploratory data analysis ✅ Feature engineering ✅ Data transformation	🤖 Machine Learning ✅ ML fundamentals ✅ Supervised learning ✅ Unsupervised learning ✅ Model training ✅ Model evaluation ✅ Scikit-learn library ✅ Algorithm selection ✅ Performance metrics
🗄️ Databases ✅ SQL queries (SELECT, JOIN) ✅ Database design ✅ CRUD operations ✅ Aggregations & grouping ✅ Subqueries ✅ Views & indexes ✅ Stored procedures ✅ Query optimization	🕷️ Web Scraping ✅ HTTP protocol ✅ HTML structure ✅ CSS selectors ✅ BeautifulSoup parsing ✅ Requests library ✅ Data extraction ✅ Ethical scraping ✅ Pipeline building	📈 Statistics ✅ Probability theory ✅ Distributions ✅ Conditional probability ✅ Bayes theorem ✅ Hypothesis testing ✅ Statistical inference ✅ Sampling techniques ✅ Error metrics

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🛠️ Technology Stack

Core Technologies

Category	Tools
💻 Language	Python 3.11+
📊 Data Analysis	NumPy, Pandas
📈 Visualization	Matplotlib, Seaborn
🕸️ Web Scraping	Requests, BeautifulSoup4
🗄️ Database	MySQL
🤖 Machine Learning	Scikit-learn
📓 IDE	Jupyter Notebook, VS Code

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📈 Progress Tracker

Use this checklist to track your learning journey:

Core Modules

• 🎓 Introduction to Data Science
• 🐍 Python Fundamentals (18 notebooks)
• 🔢 NumPy Mastery (5 notebooks)
• 🐼 Pandas Deep Dive (2 notebooks)
• 📊 Data Visualization (8 notebooks)
• 🕷️ Web Scraping (2 notebooks)
• 🗄️ SQL & Databases (20 tutorials)
• 📈 Probability & Statistics
• 🤖 Machine Learning Introduction
• 🔧 Scikit-learn Basics
• 📋 ML Algorithm Types
• 🎯 ML Practice (5+ notebooks)

Projects

• 🌐 Coders of Delhi - Social Network
• 📚 Book Data Scraper
• 🌸 Iris Classification
• 📊 Data Analysis Projects

Milestones

• 🎖️ Completed first 50 notebooks
• 🏆 Built 3 portfolio projects
• 🚀 Trained first ML model
• ⭐ Contributed to the repo

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🤝 Connect

Let's Learn Together!

Questions? Suggestions? Want to collaborate?
Feel free to open an issue or reach out directly!

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🤝 Contributing

We welcome contributions from the community! Here's how you can help:

Ways to Contribute

• 🐛 Report Bugs: Found an error? Let us know!
• 💡 Suggest Features: Have ideas for new content?
• 📝 Improve Documentation: Help make explanations clearer
• 🎨 Add Examples: Share your own projects and solutions
• 🌐 Translate: Help make content accessible in other languages

How to Contribute

1. Fork this repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

TL;DR: You can use, modify, and distribute this content freely. Attribution appreciated! 🙏

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⭐ Show Your Support

If this repository helped you in your data science journey:

• ⭐ Star this repository
• 🍴 Fork it for your own learning
• 📢 Share with fellow learners
• 💬 Spread the word on social media

📊 Repository Stats

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🙏 Acknowledgments

• 🎓 Inspired by various data science courses and bootcamps
• 📚 Built with passion for the data science community
• 🌟 Thanks to all contributors and learners

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Made with ❤️ for Data Science Learners Worldwide

Happy Learning! 🚀