Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science?
Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include:

• A/B Testing
• Anomaly Detection
• Association Rules
• Clustering
• Decision Trees and Random Forests
• Regression Analysis
• Social Network Analysis
• Neural Networks

Features:

• Intuitive explanations and visuals
• Real-world applications to illustrate each algorithm
• Point summaries at the end of each chapter
• Reference sheets comparing the pros and cons of algorithms
• Glossary list of commonly-used terms

With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

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