📁 Repository Structure

📊 Data Science & Analytics

Location: /Data/

Contains essential resources for data science, machine learning, and statistical analysis:

• Hands On Machine Learning with Scikit-Learn Keras and TensorFlow.pdf

  • Comprehensive guide to practical machine learning implementation
  • Covers scikit-learn, Keras, and TensorFlow frameworks

• Practical Statistics for Data Scientist.pdf

  • Statistical foundations essential for data science
  • Real-world applications and practical examples

• Python Data Science Handbook ESSENTIAL TOOLS FOR WORKING WITH DATA.pdf

  • Complete reference for Python-based data science
  • Covers NumPy, Pandas, Matplotlib, and Scikit-learn

• Python for Data Analysis DATA WRANGLING WITH PANDAS, NUMPY, AND IPYTHON.pdf

  • Authoritative guide to data manipulation and analysis
  • Expert techniques for data wrangling and exploration

💹 Financial Markets & Quantitative Finance

Location: /Financial Markets/

Comprehensive collection covering algorithmic trading and financial market analysis:

• Algorithmic Trading Winning Strategies and Their Rationale.pdf

  • Proven algorithmic trading strategies with theoretical foundations
  • Performance analysis and risk management techniques

• Algorithmic Trading and DMA An Introduction to Direct Access Trading Strategies.pdf

  • Direct Market Access (DMA) trading fundamentals
  • Low-latency trading infrastructure and strategies

• Inside the Black Box A Simple Guide to Systematic Investing.pdf

  • Quantitative investment strategies demystified
  • Systematic approach to portfolio management

• Quantitative Trading How to Build Your Own Algorithmic Trading Business.pdf

  • Complete guide to establishing algorithmic trading operations
  • Business aspects of quantitative trading

• Trading and Exchanges Market Microstructure for Practitioners.pdf

  • Market microstructure analysis for practical trading
  • Understanding exchange mechanisms and market dynamics

🔢 Mathematics & Engineering

Location: /Mathematics/

Fundamental mathematical resources for technical and scientific computing:

• Advanced Engineering Mathematics by Erwin Kreyszig.pdf

  • Comprehensive mathematical methods for engineering applications
  • Differential equations, linear algebra, and complex analysis

• Discrete Mathematics and Its Applications.pdf

  • Essential discrete mathematics for computer science
  • Graph theory, combinatorics, and mathematical logic

• Schaum's Outline of Linear Algebra.pdf

  • Complete linear algebra reference with solved problems
  • Vector spaces, eigenvalues, and matrix operations

• Schaum's Outline of Probability and Statistics.pdf

  • Statistical theory and probability distributions
  • Hypothesis testing and statistical inference

💻 Programming & Computer Science

Location: /Programming/

Core computer science and programming references:

• Data Structures and Algorithm Analysis in C++.pdf

  • Comprehensive algorithms and data structures guide
  • Implementation-focused approach with C++ examples

• Silberschatz−Korth−Sudarshan Database System Concepts.pdf

  • Authoritative database systems textbook
  • DBMS fundamentals, design, and implementation

🚀 Getting Started

Quick Start

1. Clone the Repository

   git clone https://github.com/DivyamSamarwal/Mahamaya.git
   cd Mahamaya

2. Navigate to Your Area of Interest

   # For Data Science resources
   cd Data/
   
   # For Financial Markets materials
   cd "Financial Markets"/
   
   # For Mathematics references
   cd Mathematics/
   
   # For Programming resources
   cd Programming/

🔗 Related Resources

• MIT OpenCourseWare - Free online course materials
• Khan Academy - Interactive learning platform
• Coursera - Online learning with university partnerships