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Complete Data Analysis Bootcamp

A comprehensive collection of data analysis materials, tutorials, and projects based on Krish Naik's Complete Data Analyst Bootcamp From Basics To Advanced Udemy course.

📚 Course Structure

This repository is organized into six main sections covering the complete data analysis workflow:

1. Python Programming 🐍

  • Python Basics - Fundamental concepts and syntax
  • Control Flow - Conditional statements and loops
  • Data Structures - Lists, tuples, dictionaries, sets
  • Functions - Function definition, parameters, lambda functions
  • Modules - Importing and creating modules
  • File Handling - Reading and writing files
  • Exception Handling - Try-except blocks and error management
  • Class and Objects - Object-oriented programming concepts
  • Advanced Python Concepts - Decorators, generators, comprehensions
  • Data Analysis With Python - NumPy, Pandas, Matplotlib, Seaborn
  • Working With Databases - Database connectivity and operations
  • Logging in Python - Logging configuration and implementation
  • Multithreading and Multiprocessing - Concurrent programming
  • Memory Management - Memory optimization techniques
  • Flask - Web development basics
  • Streamlit - Building data applications

2. Statistics 📊

  • Basics - Fundamental statistical concepts
  • Descriptive Statistics - Measures of central tendency and dispersion
  • Inferential Statistics & Hypothesis Testing - Statistical inference and testing

3. Probability 🎲

Comprehensive coverage of probability distributions and concepts:

  • Bernoulli, Binomial, Poisson Distributions
  • Normal/Gaussian Distribution
  • Standard Normal Distribution and Z-scores
  • Uniform Distribution
  • Log Normal Distribution
  • Power Law Distribution
  • Pareto Distribution
  • Central Limit Theorem
  • Estimates and Estimation Theory

4. Exploratory Data Analysis (EDA) & Feature Engineering 🔍

  • Handling Missing Values - Techniques for dealing with missing data
  • Handling Imbalance Dataset - Addressing class imbalance
  • SMOTE - Synthetic Minority Over-sampling Technique
  • Handling Outliers - Outlier detection and treatment
  • Encoding Techniques:
    • Nominal or One-Hot Encoding
    • Label and Ordinal Encoding
    • Target Guided Ordinal Encoding
  • Real-world Projects:
    • Wine Quality EDA
    • Flight Price Prediction EDA
    • Google Play Store EDA

5. SQL 💾

  • SQL Basics - Fundamental SQL queries and operations
  • SQL Functions - Built-in and aggregate functions
  • Advanced SQL - Complex queries, joins, and optimization
  • Important Interview Questions - Common SQL interview problems

6. Power BI 📈

  • Interview Questions - Comprehensive Power BI interview preparation materials

🛠️ Requirements

Python Dependencies

pip install -r 1-PYTHON/requirements.txt

Required packages:

  • numpy - Numerical computing
  • pandas - Data manipulation and analysis
  • matplotlib - Data visualization
  • seaborn - Statistical data visualization
  • scikit-learn - Machine learning library
  • flask - Web framework
  • streamlit - Data app framework
  • memory_profiler - Memory usage profiling
  • ipykernel - Jupyter kernel support

🚀 Getting Started

  1. Clone the repository:

    git clone https://github.com/Suraj-G-Rao/Complete_Data_Analysis.git

  2. Navigate to the project directory:

    cd Complete_Data_Analysis

  3. Install Python dependencies:

    pip install -r 1-PYTHON/requirements.txt

  4. Start learning:

    • Begin with Python basics in 1-PYTHON/1-Python Basics/
    • Progress through each section sequentially
    • Practice with the provided Jupyter notebooks

📁 Project Structure

Complete_Data_Analysis/
├── 1-PYTHON/                    # Python programming tutorials
├── 2-Statistics/                # Statistical concepts and methods
├── 3-Probability/               # Probability theory and distributions
├── 4-EDA & Feature Engineering/ # Data exploration and preprocessing
├── 5. SQL/                      # Database querying and management
├── 6-POWER BI/                  # Business intelligence and visualization
├── requirements.txt             # Python dependencies
├── LICENSE                      # Project license
└── README.md                    # This file

🎯 Learning Path

  1. Foundation: Start with Python programming fundamentals
  2. Mathematics: Build strong statistical and probability knowledge
  3. Data Handling: Learn EDA and feature engineering techniques
  4. Database Skills: Master SQL for data extraction
  5. Visualization: Create impactful dashboards with Power BI

💡 Key Features

  • Comprehensive Coverage: From basics to advanced topics
  • Practical Examples: Real-world datasets and projects
  • Step-by-Step Learning: Structured curriculum progression
  • Interview Preparation: SQL and Power BI interview questions
  • Hands-on Practice: Jupyter notebooks for interactive learning

📖 Course Reference

This repository follows the curriculum from:

  • Course: Complete Data Analyst Bootcamp From Basics To Advanced
  • Instructor: Krish Naik
  • Platform: Udemy

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

⭐ Acknowledgments

  • Krish Naik for the comprehensive data analysis bootcamp course
  • The data science community for continuous learning and support

Happy Learning! 🚀