Description
Data Science from Scratch: First Principles with Python (2nd Edition) by Joel Grus is one of the most widely recommended introductory books for anyone entering the world of data science. This edition has been fully updated for modern Python practices and includes expanded coverage of machine learning, deep learning, probability, statistics, and data analysis tools.
Rather than relying on pre-built libraries, the book explains data science concepts by building them from scratch, enabling readers to understand how algorithms truly work. Readers learn Python programming, mathematical foundations, data visualization, supervised and unsupervised learning, neural networks, recommendation systems, and much more.
This book is perfect for students, coding enthusiasts, data analysts, and professionals who want to develop a strong conceptual understanding of data science and the Python ecosystem.
⭐ Key Features:
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Teaches core data science concepts from first principles
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Step-by-step Python implementation of algorithms
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Covers statistics, probability, linear algebra, & calculus basics
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Machine learning algorithms explained in detail
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Neural networks & deep learning introduced clearly
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Hands-on coding examples for practical learning
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Suitable for beginners and self-learners
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Updated Python 3 content and fresh examples
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Ideal for career beginners in data science, AI, ML, and analytics
