Data Science from Scratch is a hands-on introduction to data science that emphasizes learning core concepts by implementing them from the ground up. Written by Joel Grus, this book breaks down essential topics such as statistics, machine learning, data visualization, and deep learning, guiding readers through coding their own algorithms in Python.

Instead of relying on pre-built libraries, the book teaches fundamental data science principles by building models from scratch, helping readers understand how algorithms work under the hood. With a focus on practical coding and mathematical foundations, it is ideal for beginners looking to develop a solid foundation in data science.

Why Read This Book

• Learn data science fundamentals through coding, without relying on libraries like scikit-learn or TensorFlow.
• Covers essential topics such as probability, statistics, linear algebra, and machine learning with practical examples.
• Hands-on approach with Python code implementations for key algorithms.
• Helps readers develop an intuitive understanding of data science by focusing on first principles.
• Ideal for aspiring data scientists, programmers, and analysts looking to break into the field.

About the Author

Joel Grus is a data scientist, software engineer, and educator with experience in both academia and industry. He has worked at companies like Google and Allen Institute for Artificial Intelligence, contributing to machine learning research and applications. His teaching emphasizes understanding how and why data science techniques work, making him a trusted voice in the field.