📊 Data Science Learning Journey

Master Data Science from Fundamentals to Machine Learning

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

🌟 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!

📚 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

🚀 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!

📖 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]

📅 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

🎯 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

💡 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

🛠️ 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

📈 Progress Tracker

Use this checklist to track your learning journey:

Core Modules

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

🤝 Connect

Let's Learn Together!

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

🤝 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

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

📜 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! 🙏

⭐ 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

🙏 Acknowledgments

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

Made with ❤️ for Data Science Learners Worldwide

Happy Learning! 🚀

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